row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
42,869
|
Hello
|
a9ba95f3b9c0253bb1565aa59f8b3e8b
|
{
"intermediate": 0.3123404085636139,
"beginner": 0.2729349136352539,
"expert": 0.4147246778011322
}
|
42,870
|
In my react app I have a div container with two other containers. The first one contains an input and the second one contains an icon. Icon containers follows input container in the layout. The problem is that when I type in the input and then need to click the button the button is visible but cannot be clicked even though visually it is inside input. I tried adding z-index to the icon but it didn't work. What can I do in order for my click to reach it?
|
905b5fa9410971d7a6be136552a90f54
|
{
"intermediate": 0.48623794317245483,
"beginner": 0.26981794834136963,
"expert": 0.24394409358501434
}
|
42,871
|
hi
|
2353c3e1144c65c0798c74d8cb643029
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
42,872
|
rust insert an Option value into hashmap, and don't do the insert if value is None
|
0f3744fc4dde602840b008da1212e1fa
|
{
"intermediate": 0.4119317829608917,
"beginner": 0.22723262012004852,
"expert": 0.36083558201789856
}
|
42,873
|
########################################################################
# IMPORT STATEMENT BELOW #
########################################################################
import csv
########################################################################
# HELPER FUNCTIONS #
########################################################################
def set_stage():
""" Sets the stage, time, and gets the player's name """
time = 0
time_display = codesters.Display(time)
stage.set_background("space")
stage.disable_all_walls()
ufo = codesters.Sprite("ufo")
ufo.set_size(0.5)
player_name = ufo.ask("Please enter your name.")
ufo.say(player_name)
return ufo, player_name, time_display, time
def play_game():
""" Play game until user gets hit """
ufo, player_name, time_display, time = set_stage()
add_sprite_events(ufo)
while not is_hit:
wait_time = round(1.0/(1 + time/10.0), 1)
time += wait_time
time_display.update(time)
y = random.randint(-225, 225)
meteor = codesters.Sprite("meteor1", -400, y)
meteor.set_x_speed(10 + time/5.0)
meteor.set_size(0.5)
stage.wait(wait_time)
return player_name, float(time)
def add_sprite_events(sprite):
""" Add the key and collision event to sprite """
global is_hit
is_hit = False
def collision(sprite, hit_sprite):
global is_hit
is_hit = True
hit_sprite.hide()
sprite.turn_right(360)
sprite.hide()
sprite.event_collision(collision)
def left_key():
sprite.set_x(sprite.get_x() - 20)
stage.event_key("left", left_key)
def right_key():
sprite.set_x(sprite.get_x() + 20)
stage.event_key("right", right_key)
def up_key():
if sprite.get_y() < 215:
sprite.set_y(sprite.get_y() + 20)
stage.event_key("up", up_key)
def down_key():
if sprite.get_y() > -215:
sprite.set_y(sprite.get_y() - 20)
stage.event_key("down", down_key)
def sort_by_score(data):
""" Sort a list of lists by it's second column """
data.sort(reverse = True, key = lambda x: x[1])
########################################################################
# CSV AND FILE OPEN #
########################################################################
def get_high_scores(file_name):
""" Returns data from CSV as list of lists """
my_file = open(file_name, "r")
csv_contents = []
my_data = csv.reader(my_file)
for row in my_data:
print(row)
row[1] = float(row[1])
csv_contents.append(row)
print("")
my_file.close()
return csv_contents
def update_high_scores(file_name, my_data):
""" Uses list of lists to write to new file """
my_file = open(file_name, "w")
data_writer = csv.writer(my_file)
for row in my_data:
data_writer.writerow(row)
my_file.close()
########################################################################
# MAIN FUNCTION #
########################################################################
def main():
""" Sets up the program and calls other functions """
player_name, time = play_game()
data = get_high_scores("updated_scores.csv")
data.append([player_name, time])
sort_by_score(data)
print(data)
update_high_scores("updated_scores.csv", data)
new_data = get_high_scores("updated_scores.csv")
main()
########################################################################
Now customize and extend your project! Make sure to meet these minimum technical requirements:
Write a new function in your code called display_high_scores() that takes in one parameter called data.
Inside display_high_scores(), use a method of your choice to display the contents of data.
Call display_high_scores() at the bottom of main() and make sure to pass in new_data as the parameter.
|
05ff37d84ff28f13b44fe16496d6b1d7
|
{
"intermediate": 0.291224867105484,
"beginner": 0.5362494587898254,
"expert": 0.17252564430236816
}
|
42,874
|
Create a VBA for excel to delte a row if a cell contains value TRUE. The Cell is in column C and begins from C8 to C1100
|
55646c51e9a7dec1646529e8700e076d
|
{
"intermediate": 0.49952319264411926,
"beginner": 0.188284233212471,
"expert": 0.31219255924224854
}
|
42,875
|
api: https://api.cryptodatadownload.com/v1/data/ohlc/binance/guest/spot/available?_=1710763945638
the provided api returns list of all available files , and download link for each file came as “file” parameter like :
{
“data”: [
{
“symbol”: “1INCHBTC”,
“timeframe”: “hour”,
“start_date”: “2020-12-25 05:00:00”,
“end_date”: “2024-03-16 23:00:00”,
“file”: “https://www.cryptodatadownload.com/cdd/Binance_1INCHBTC_1h.csv”
},
{
“symbol”: “1INCHBTC”,
“timeframe”: “2021 minute”,
“start_date”: “2021-01-01 00:00:00”,
“end_date”: “2023-03-26 23:59:00”,
“file”: “https://www.cryptodatadownload.com/cdd/Binance_1INCHBTC_2021_minute.csv”
},
give me the proper code to fetch the download links from provided api and download them
|
d65965c966c7f1855f6317902e1d49ab
|
{
"intermediate": 0.5819525718688965,
"beginner": 0.2394198477268219,
"expert": 0.1786275953054428
}
|
42,876
|
var relGlide = new GlideRecord("cmdb_rel_ci);
var queryString = "type=d93304fb0a0a0b78006081a72ef08444^child.ip_address=" +current.variables.ip_address;
relGlide.addEncodedQuery(queryString);
relGlide.query();
workflow.scratchpad.vm_flag = false;
while (relGlide.next()){
//if any CI is operational
if(relGlide.parent.operational_status ==1){
workflow.scratchpad.vm_flag = true;
}
}
change this functionality into catalog client script code and show popup error if this functionality didn't work
|
d385d60229f4d8c7eec96b0d164ad82f
|
{
"intermediate": 0.3233695328235626,
"beginner": 0.3443104922771454,
"expert": 0.3323199450969696
}
|
42,877
|
how can i highlight rows based on a cells value so say i had a range of a1 to c100 and i wanted to highlight all the rows where the value "true" shows up in each cell in the C column
|
2495be8b3cf0d70683d6774b042e6c4b
|
{
"intermediate": 0.471502423286438,
"beginner": 0.15340504050254822,
"expert": 0.3750925660133362
}
|
42,878
|
how can i highlight rows based on a cells value so say i had a range of a1 to c100 and i wanted to highlight all the rows where the value “true” shows up in each cell in the C column
|
f281f5966830bb4995834d13f3de13c7
|
{
"intermediate": 0.45602506399154663,
"beginner": 0.14767760038375854,
"expert": 0.3962973356246948
}
|
42,879
|
how can i select rows based on a cells value so say i had a range of a1 to c100 and i wanted to highlight all the rows where the value “true” shows up in each cell in the C column
|
d3f71739e88f4539a9dbe6b9e928a6dd
|
{
"intermediate": 0.44775888323783875,
"beginner": 0.16604886949062347,
"expert": 0.3861922323703766
}
|
42,880
|
请详细解释以下代码：messages = [
{“role”: “user”, “content”: “Write an essay about Generative AI.”},
]
text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(text, return_tensors=“pt”)
inputs = {k: v.to(“cuda”) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0]))
|
ca0b0e9560a2f85ee4c6029a3402aa8d
|
{
"intermediate": 0.24384251236915588,
"beginner": 0.27915480732917786,
"expert": 0.47700265049934387
}
|
42,881
|
import json
from flask import Flask, request, render_template
import socket
import os
app = Flask(__name__)
def is_port_open(ip, port):
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.settimeout(5)
try:
sock.connect((ip, port))
return True
except socket.error:
return False
finally:
sock.close()
@app.route('/', methods=['GET', 'POST'])
def index():
result = None
ip = ""
port = ""
if request.method == 'POST':
ip = request.form.get('ip')
port = request.form.get('port')
try:
port = int(port)
if 0 < port < 65536: # Port number should be between 1 and 65535
result = is_port_open(ip, port)
else:
result = "Invalid port number."
except ValueError:
result = "Invalid IP address or port number."
except Exception as e:
result = f"An error occurred: {str(e)}"
return render_template('index.html', result=result, ip=ip, port=port)
@app.errorhandler(Exception)
def handle_exception(e):
# pass through HTTP errors
if isinstance(e, HTTPException):
return e
# now you're handling non-HTTP exceptions only
response = {
"error": str(e),
}
return jsonify(response), 500
if __name__ == "__main__":
app.run(debug=False, host='0.0.0.0', port=5000)
write a serverless function for above code to run on netlify
|
f291f014244fc5d6bb2d472ed9d575bb
|
{
"intermediate": 0.6039605140686035,
"beginner": 0.19692818820476532,
"expert": 0.19911132752895355
}
|
42,882
|
I am having 13 input_parameters ['L1', 'L3', 'L5', 'L6', 'L7', 'W1', 'W3', 'W5', 'W6', 'W7', 'Io', 'Cp', 'Vc'] to be tune to get the target output all transistor must be in saturation 'saturation_condition', and '7' objective performance metric values 'area', 'power dissipation', 'DC gain', 'Slew rate', 'Bandwidth3dB', 'Unity gain bandwidth', and 'Phase margin'.
input_parameters ['L1', 'L3', 'L5', 'L6', 'L7', 'W1', 'W3', 'W5', 'W6', 'W7', 'Io', 'Cp', 'Vc'] has constraints:
bounds_low = np.array([0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 0.18e-6, 0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 0.5e-6, 15e-6, 0.1e-12, 0.8])
bounds_high = np.array([0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 0.2e-6, 50e-6, 50e-6, 50e-6, 50e-6, 50e-6, 30e-6, 10e-12, 1.4])
Target output need to be achieve within the constraints:
target_metrics = {
'Area': (1e-12, 1e-10),
'PowerDissipation': (0.005, 0.25),
'SlewRate': (20, 60),
'Gain': (70, 100),
'Bandwidth3dB': (30e3, 100e3),
'UnityGainFreq': (30e6, 100e6),
'PhaseMargin': (65, 90)
From my Circuit environment,
I am having the normalized State space representation: Normalized continuous variables (13), One-hot encoded regions (24), binary saturation state (1), normalized performance metrics (7).
I am having 'reward' generation function which properly adapts the strategy of my requirement of objectives.
I need you to implement RL algorithm with policy network for PPO (Proximal Policy Optimization) in an actor-critic setting involves designing two neural networks: the actor and the critic. The actor network maps the state space to the action space suggesting actions to take, while the critic network estimates the value of the state-action pairs, essentially critiquing the actions proposed by the actor.
The implementation must provide me the values of 'action' in continuous action space, to execute the below statement.
'next_state, reward, done, previous_metrics = env.step(action, perf_metrics)'
Continuous Action space of 13 variables ['L1', 'L3', 'L5', 'L6', 'L7', 'W1', 'W3', 'W5', 'W6', 'W7', 'Io', 'Cp', 'Vc'] are must be with in its bounds.
to implement the RL algorithm you have the
# Training Loop
for episode in range(num_episodes):
# Define the Training Loop
state, performance_metrics = env.reset()
state space (normalized) (45)
current 'input_parameters' continuous variables (13),
for a transistor in region 1, the one-hot encoding would be [1, 0, 0]. For a transistor in saturation (region 2), it would be [0, 1, 0], and for region 3 it would be [0, 0, 1], For 8 transistors, One-hot encoded regions (24),
Consolidated status of all 8 transistors, binary indicator that specifies whether they are all in saturation (1) or not (0), binary saturation state (1),
current normalized performance metrics 'area', 'power dissipation', 'DC gain', 'Slew rate', 'Bandwidth3dB', 'Unity gain bandwidth', and 'Phase margin' (7).
|
645faa308456a6c2576a9b6944400f9c
|
{
"intermediate": 0.26643842458724976,
"beginner": 0.5348803400993347,
"expert": 0.19868117570877075
}
|
42,883
|
In the below function 'select_action', the 'action_mean', from the computation 'action_mean, action_log_std = actor(state_tensor)' gives the result between -1,1. After that we are computing 'normal_dist' using the 'normal_dist = torch.distributions.Normal(action_mean, action_std), action = normal_dist.sample()', here we are getting the results in wide ranges in negative and positive sides, then after clipping the 'action' from normal distribution leads the action space values only in the boundaries either bounds_low or bounds_high, I am not getting the ranges of values between the bounds for all variables. what should we do now. Does scaling the 'action_mean' or the 'action' after normal distribution between 0 to 1, does make any impact or not?
# Select Action Function
def select_action(state, actor, critic, bounds_low, bounds_high, performance_metrics):
state_tensor = torch.from_numpy(state).float().unsqueeze(0)
action_mean, action_log_std = actor(state_tensor)
print("action_mean",action_mean)
action_std = action_log_std.exp()
# Create a normal distribution and sample an action
normal_dist = torch.distributions.Normal(action_mean, action_std)
action = normal_dist.sample()
print("action normal dist", action)
log_prob = normal_dist.log_prob(action).sum(dim=1, keepdim=True)
# Clip your actions to their limits
#action = action.clamp(torch.tensor(bounds_low).unsqueeze(0), torch.tensor(bounds_high).unsqueeze(0))
action = action.clamp(torch.tensor(bounds_low).unsqueeze(0), torch.tensor(bounds_high).unsqueeze(0) - torch.tensor(bounds_low).unsqueeze(0))
# Estimate the value of the state using the critic network
value = critic(state_tensor)
return action.numpy(), log_prob, value.squeeze(), performance_metrics
|
7a3262df599742445760cefd348ca855
|
{
"intermediate": 0.34357282519340515,
"beginner": 0.3023482859134674,
"expert": 0.35407888889312744
}
|
42,884
|
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Document</title>
<link href="style.css" rel="stylesheet">
<script src="calc.js"></script>
</head>
<body>
<div id="container">
<div id="screen">0</div>
<div>7</div>
<div>8</div>
<div>9</div>
<div>C</div>
<div>4</div>
<div>5</div>
<div>6</div>
<div>+</div>
<div>1</div>
<div>2</div>
<div>3</div>
<div>-</div>
<div class="double"</div>
<div>=</div>
</body>
</html> zmien zeby bylo git
|
1787701c6d01e703d82e4dfd6f9d4033
|
{
"intermediate": 0.3714441657066345,
"beginner": 0.35671502351760864,
"expert": 0.2718408703804016
}
|
42,885
|
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Document</title>
<link href="style.css" rel="stylesheet">
<script src="calc.js"></script>
</head>
<body>
<div id="container">
<div id="screen">0</div>
<div>7</div>
<div>8</div>
<div>9</div>
<div>C</div>
<div>4</div>
<div>5</div>
<div>6</div>
<div>+</div>
<div>1</div>
<div>2</div>
<div>3</div>
<div>-</div>
<div class="double"</div>
<div>=</div>
</body>
</html> zmien kod zeby bylo git
|
855b369b828375a0e20b6347117b71bd
|
{
"intermediate": 0.347430557012558,
"beginner": 0.36651116609573364,
"expert": 0.286058247089386
}
|
42,886
|
comment mettre en place la page de classement en flutter dart avec une colonne rang, pseudo, temps, et score ? (j'utilise sqflite)import 'package:flutter/material.dart';
import 'package:flutter_snake/ui/snake_page.dart';
import 'package:flutter_snake/ui/classement_page.dart';
class MyHomePage extends StatefulWidget {
const MyHomePage({Key? key}) : super(key: key);
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _currentIndex = 0;
final List<Widget> _children = [
SnakePage(),
ClassementPage(),
];
// on met les pages ici après
void onTabTapped(int index) {
setState(() {
_currentIndex = index;
});
}
@override
Widget build(BuildContext context) {
return Scaffold(
body: Center(
child: Column(
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
ElevatedButton(
child: Text('Jouer'),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => SnakePage()),
);
},
),
ElevatedButton(
child: Text('Classement'),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => ClassementPage()),
);
},
),
ElevatedButton(
child: Text('Règles'),
onPressed: () {
// Remplacez ceci par la navigation vers votre page de règles
},
),
],
),
),
);
}
}
import 'package:flutter/material.dart';
class ClassementPage extends StatelessWidget {
const ClassementPage({Key? key}) : super(key: key);
@override
Widget build(BuildContext context) {
return const Center(child: Text('Page Class'));
}
}
|
3d3bf84ec9f3da82d6bf5694a673925a
|
{
"intermediate": 0.2850206792354584,
"beginner": 0.39532405138015747,
"expert": 0.31965523958206177
}
|
42,887
|
On docker, how to avoid to mount volume when you need a config file ?
|
e1cac43ce8506aadbdc570f0edd7c4ff
|
{
"intermediate": 0.36310887336730957,
"beginner": 0.3728332221508026,
"expert": 0.2640579342842102
}
|
42,888
|
I have multiple docker compose, on multiple machine. How to use one file (for example, a config file) on thoses docker compose ?
|
6a2f87691dad00b7de1eb26479b0c463
|
{
"intermediate": 0.3627372682094574,
"beginner": 0.40552079677581787,
"expert": 0.23174196481704712
}
|
42,889
|
comment mettre en place la page de classement en flutter dart avec une colonne rang, pseudo, temps, et score pour un jeu de snake ? (j’utilise sqflite ensuite)import ‘package:flutter/material.dart’;
import ‘package:flutter_snake/ui/snake_page.dart’;
import ‘package:flutter_snake/ui/classement_page.dart’;
class MyHomePage extends StatefulWidget {
const MyHomePage({Key? key}) : super(key: key);
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _currentIndex = 0;
final List<Widget> _children = [
SnakePage(),
ClassementPage(),
];
// on met les pages ici après
void onTabTapped(int index) {
setState(() {
_currentIndex = index;
});
}
@override
Widget build(BuildContext context) {
return Scaffold(
body: Center(
child: Column(
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
ElevatedButton(
child: Text(‘Jouer’),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => SnakePage()),
);
},
),
ElevatedButton(
child: Text(‘Classement’),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => ClassementPage()),
);
},
),
ElevatedButton(
child: Text(‘Règles’),
onPressed: () {
// Remplacez ceci par la navigation vers votre page de règles
},
),
],
),
),
);
}
}
import ‘package:flutter/material.dart’;
class ClassementPage extends StatelessWidget {
const ClassementPage({Key? key}) : super(key: key);
@override
Widget build(BuildContext context) {
return const Center(child: Text(‘Page Class’));
}
}import 'package:flutter/material.dart';
import 'package:flutter_snake/ui/snake_page.dart';
import 'package:flutter_snake/ui/classement_page.dart';
class MyHomePage extends StatefulWidget {
const MyHomePage({Key? key}) : super(key: key);
@override
_MyHomePageState createState() => _MyHomePageState();
}
class _MyHomePageState extends State<MyHomePage> {
int _currentIndex = 0;
final List<Widget> _children = [
SnakePage(),
ClassementPage(),
];
// on met les pages ici après
void onTabTapped(int index) {
setState(() {
_currentIndex = index;
});
}
@override
Widget build(BuildContext context) {
return Scaffold(
body: Center(
child: Column(
mainAxisAlignment: MainAxisAlignment.center,
children: <Widget>[
ElevatedButton(
child: Text('Jouer'),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => SnakePage()),
);
},
),
ElevatedButton(
child: Text('Classement'),
onPressed: () {
Navigator.push(
context,
MaterialPageRoute(builder: (context) => ClassementPage()),
);
},
),
ElevatedButton(
child: Text('Règles'),
onPressed: () {
// Remplacez ceci par la navigation vers votre page de règles
},
),
],
),
),
);
}
}
|
7596075c25cdf4bdfc5cdb50296b7e89
|
{
"intermediate": 0.34992870688438416,
"beginner": 0.3305101990699768,
"expert": 0.31956103444099426
}
|
42,890
|
What model are you using ?
|
b94ffb454d650aa01c8168bbd0f3efb0
|
{
"intermediate": 0.1961696743965149,
"beginner": 0.14496909081935883,
"expert": 0.6588611602783203
}
|
42,891
|
Here are the instructions in English:
Translate the following legal text into colloquial Persian.
Place the English and Persian text side by side in a table.
From the beginning to the end of the text, there should be an English sentence on the left side and a Persian sentence on the right side.
Use legal language for the Persian translation.
Place the English and Persian text line by line next to each other in such a way that one line of English text is followed by two empty lines, and then the Persian translation, and continue this process until the end of the text.4. Contractual Intention Even though the parties have reached agreement, and even tough tat agrecment is supported by considcration, tere may stll be no contact because the agrecment was made without any intention to affect lcgal relations. Of course, in te vast majority of commercial tansactions tere is no need affiratively to prove tat te partics had such an intention. On te contary, it is up to the party denying te existence of a contact to disprove te intention and the onus of proof which he bas to discharge is a heavy one. Morcover. in deciding the issue of contactual intenton. thc courts apply an objective test, so that a party to an ordinary commercial arrangement cannot escape liability merely by showing that he did not, in bis own mind, intend the agrecment to affect legal relations.
4. Contractual Intention Even though the parties have reached agreement, and even tough tat agrecment is supported by considcration, tere may stll be no contact because the agrecment was made without any intention to affect lcgal relations. Of course, in te vast majority of commercial tansactions tere is no need affiratively to prove tat te partics had such an intention. On te contary, it is up to the party denying te existence of a contact to disprove te intention and the onus of proof which he bas to discharge is a heavy one. Morcover. in deciding the issue of contactual intenton. thc courts apply an objective test, so that a party to an ordinary commercial arrangement cannot escape liability merely by showing that he did not, in bis own mind, intend the agrecment to affect legal relations.
|
a22e5f5f83a4bd026e26048148abdfad
|
{
"intermediate": 0.28667083382606506,
"beginner": 0.38015085458755493,
"expert": 0.33317831158638
}
|
42,892
|
How can you specify scan targets when using the OWASP ZAP tool
|
ca315011d05f090d71f46e9a1e16c8dd
|
{
"intermediate": 0.3983900547027588,
"beginner": 0.14604948461055756,
"expert": 0.45556044578552246
}
|
42,893
|
give me a proper python code to convert json to csv
|
6d482cb2de585d22c52a6422a49f933d
|
{
"intermediate": 0.551441490650177,
"beginner": 0.1561233401298523,
"expert": 0.2924351394176483
}
|
42,894
|
string message = StringFormat(
"AboveCurrentPriceLevel: %f "
"BelowCurrentPriceLevel: %f "
"PipsToChange: %ld "
"InitialLots: %f "
"LotChangePercent: %f "
"TimeFrame: %d "
"PipsToDown: %ld "
"PipsToUp: %ld "
"Symbol: %s "
"Номер счета: %f",
AboveCurrentPriceLevel,
BelowCurrentPriceLevel,
PipsToChangeint,
InitialLots,
LotChangePercent,
TimeInt,
PercentToDownInt,
PercentToUpInt,
_Symbol,
AccountInfoInteger(ACCOUNT_LOGIN)); как в номере счета написать лонг
|
54f583183cd3c220d0ca74bb91291902
|
{
"intermediate": 0.26530104875564575,
"beginner": 0.4686053693294525,
"expert": 0.2660936117172241
}
|
42,895
|
How do I make my chrome extension be notified when the user is on a certain tab? What permissions do I need?
|
4f5b21da25e2580fe0246aee4749ee01
|
{
"intermediate": 0.48425403237342834,
"beginner": 0.2595047354698181,
"expert": 0.2562412917613983
}
|
42,896
|
You are a Python expert who can provide clear, concise, high-quality code.
"
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import cv2
import random
import tensorflow as tf
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk, Image
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display, clear_output
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import Adam, SGD, RMSprop, AdamW, Adadelta, Adagrad, Adamax, Adafactor, Nadam, Ftrl
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tqdm import tqdm
import os
from sklearn.utils import shuffle
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.layers import (
GlobalAveragePooling2D,
Dropout,
Dense,
Conv2D,
MaxPooling2D,
Flatten,
Dropout,
BatchNormalization,
Activation,
concatenate,
Conv2DTranspose,
Input,
Reshape,
UpSampling2D,
)
from tensorflow.keras.applications import (
EfficientNetV2B0,
EfficientNetV2B1,
EfficientNetV2B2,
EfficientNetV2B3,
EfficientNetV2L,
EfficientNetV2M,
EfficientNetV2S,
)
from tensorflow.keras.applications import Xception
from tensorflow.keras.applications import VGG16, VGG19
from tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, ResNetRS50, ResNetRS101
from tensorflow.keras.applications import InceptionResNetV2, ConvNeXtXLarge, ConvNeXtBase, DenseNet121, MobileNetV2, NASNetLarge, NASNetMobile
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint
from sklearn.metrics import classification_report, confusion_matrix
import ipywidgets as widgets
import io
from PIL import Image
from IPython.display import display, clear_output
from warnings import filterwarnings
from google.colab import drive
drive.mount("/content/gdrive")
def load_data(data_folders):
X_data = [] # Combined data
y_class_labels = [] # Combined classification labels
y_seg_labels = [] # Combined segmentation labels
for folderPath in data_folders:
for label in labels:
label_folder_path = os.path.join(folderPath, label)
for filename in tqdm(os.listdir(label_folder_path)):
if filename.endswith(".jpg"):
img = cv2.imread(os.path.join(label_folder_path, filename))
img = cv2.resize(img, (image_size, image_size))
X_data.append(img)
y_class_labels.append(label)
seg_filename = filename.split(".")[0] + ".png"
seg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0)
seg_img = cv2.resize(seg_img, (image_size, image_size))
seg_img = np.where(seg_img > 0, 1, 0) # Convert segmentation mask to binary
y_seg_labels.append(seg_img)
X_data = np.array(X_data)
y_class_labels = np.array(y_class_labels)
y_seg_labels = np.array(y_seg_labels)
X_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)
return X_data, y_class_labels, y_seg_labels
def split_data(X_data, y_class_labels, y_seg_labels, train_ratio, val_ratio, test_ratio):
num_samples = len(X_data)
num_train_samples = int(train_ratio * num_samples)
num_val_samples = int(val_ratio * num_samples)
num_test_samples = int(test_ratio * num_samples)
X_train = X_data[:num_train_samples]
y_train_class = y_class_labels[:num_train_samples]
y_train_seg = y_seg_labels[:num_train_samples]
X_val = X_data[num_train_samples: num_train_samples + num_val_samples]
y_val_class = y_class_labels[num_train_samples: num_train_samples + num_val_samples]
y_val_seg = y_seg_labels[num_train_samples: num_train_samples + num_val_samples]
X_test = X_data[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
y_test_class = y_class_labels[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
y_test_seg = y_seg_labels[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
# Convert class labels to categorical
label_encoder = LabelEncoder()
y_train_class_encoded = label_encoder.fit_transform(y_train_class)
y_train_class_categorical = to_categorical(y_train_class_encoded)
y_val_class_encoded = label_encoder.transform(y_val_class)
y_val_class_categorical = to_categorical(y_val_class_encoded)
y_test_class_encoded = label_encoder.transform(y_test_class)
y_test_class_categorical = to_categorical(y_test_class_encoded)
return (
X_train,
y_train_class_categorical,
y_train_seg,
X_val,
y_val_class_categorical,
y_val_seg,
X_test,
y_test_class_categorical,
y_test_seg,
)
def count_labels(y_class_categorical, label_encoder):
# Convert one-hot encoded labels back to label encoded
y_class_labels = np.argmax(y_class_categorical, axis=1)
# Convert label encoded labels back to original class names
y_class_names = label_encoder.inverse_transform(y_class_labels)
unique, counts = np.unique(y_class_names, return_counts=True)
return dict(zip(unique, counts))
def build_model(input_shape, num_classes):
num_filter = 32 # 16/32 best, 8: best classification but no segment
# Encoder (Done)
inputs = Input(input_shape)
conv1 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(inputs)
bn1 = BatchNormalization()(conv1)
relu1 = Activation("relu")(bn1)
conv2 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu1)
bn2 = BatchNormalization()(conv2)
relu2 = Activation("relu")(bn2)
down1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2)
conv3 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(down1)
bn3 = BatchNormalization()(conv3)
relu3 = Activation("relu")(bn3)
conv4 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu3)
bn4 = BatchNormalization()(conv4)
relu4 = Activation("relu")(bn4)
down2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4)
conv5 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down2)
bn5 = BatchNormalization()(conv5)
relu5 = Activation("relu")(bn5)
conv6 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu5)
bn6 = BatchNormalization()(conv6)
relu6 = Activation("relu")(bn6)
down3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6)
conv7 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(down3)
bn7 = BatchNormalization()(conv7)
relu7 = Activation("relu")(bn7)
conv8 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu7)
bn8 = BatchNormalization()(conv8)
relu8 = Activation("relu")(bn8)
# Middle
down4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8)
conv9 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(down4)
bn9 = BatchNormalization()(conv9)
relu9 = Activation("relu")(bn9)
conv10 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(relu9)
bn10 = BatchNormalization()(conv10)
relu10 = Activation("relu")(bn10)
up1 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu10)
# Decoder (Done)
concat1 = concatenate([up1, relu8], axis=-1) # , axis=3
conv11 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(concat1)
bn11 = BatchNormalization()(conv11)
relu11 = Activation("relu")(bn11)
conv12 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu11)
bn12 = BatchNormalization()(conv12)
relu12 = Activation("relu")(bn12)
up2 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu12)
concat2 = concatenate([up2, relu6], axis=-1) # , axis=3
conv13 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(concat2)
bn13 = BatchNormalization()(conv13)
relu13 = Activation("relu")(bn13)
conv14 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu13)
bn14 = BatchNormalization()(conv14)
relu14 = Activation("relu")(bn14)
up3 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu14)
concat3 = concatenate([up3, relu4], axis=-1) # , axis=3
conv15 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(concat3)
bn15 = BatchNormalization()(conv15)
relu15 = Activation("relu")(bn15)
conv16 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu15)
bn16 = BatchNormalization()(conv16)
relu16 = Activation("relu")(bn16)
up4 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu16)
concat4 = concatenate([up4, relu2], axis=-1) # , axis=3
conv17 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(concat4)
bn17 = BatchNormalization()(conv17)
relu17 = Activation("relu")(bn17)
conv18 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu17)
bn18 = BatchNormalization()(conv18)
relu18 = Activation("relu")(bn18)
# Segmentation branch
segmentation_output = Conv2D(1, 1, activation="sigmoid", name="segmentation_output")(relu18) # original
# Classification branch (Not done)
gap1 = GlobalAveragePooling2D()(relu8)
gap2 = GlobalAveragePooling2D()(relu10)
gap3 = GlobalAveragePooling2D()(relu12)
conv20 = Conv2D(16, 3, activation="linear", padding="same", strides=1)(segmentation_output)
bn20 = BatchNormalization()(conv20)
relu20 = Activation("relu")(bn20)
down5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20)
conv21 = Conv2D(32, 3, activation="linear", padding="same", strides=1)(down5)
bn21 = BatchNormalization()(conv21)
relu21 = Activation("relu")(bn21)
down6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21)
conv22 = Conv2D(64, 3, activation="linear", padding="same", strides=1)(down6)
bn22 = BatchNormalization()(conv22)
relu22 = Activation("relu")(bn22)
down7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22)
flatten1 = Flatten()(down7)
concat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1)
# FC layers
fc1 = Dense(1024, activation="relu")(concat5)
dropout1 = Dropout(0.5)(fc1)
fc2 = Dense(1024, activation="relu")(dropout1)
dropout2 = Dropout(0.5)(fc2)
classification_output = Dense(num_classes, activation="softmax", name="classification_output")(dropout2)
# Define the model
model = Model(inputs=inputs, outputs=[classification_output, segmentation_output])
return model
def segmentation_loss(y_true, y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
bce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
smooth = 1e-5
intersection = tf.reduce_sum(y_true * y_pred)
union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)
dice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth)
segmentation_loss = bce_loss + 1 * dice_loss
return segmentation_loss
def train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs):
checkpoint = ModelCheckpoint(
"multitask_best_weights.h5",
monitor="val_classification_output_accuracy",
save_best_only=True,
mode="max",
verbose=1,)
reduce_lr = ReduceLROnPlateau(
monitor="val_classification_output_accuracy",
factor=0.3,
patience=2,
min_delta=0.001,
mode="auto",
verbose=1,)
tensorboard = TensorBoard(log_dir="logs")
model.compile(
optimizer=Adam(lr=0.001),
loss={"classification_output": "categorical_crossentropy", "segmentation_output": segmentation_loss},
metrics={"classification_output": "accuracy", "segmentation_output": "accuracy"},
loss_weights={"classification_output": 1, "segmentation_output": 1},)
history = model.fit(
X_train,
{"classification_output": y_train_class, "segmentation_output": y_train_seg},
validation_data=(X_val, {"classification_output": y_val_class, "segmentation_output": y_val_seg}),
epochs=epochs,
verbose=1,
batch_size=batch_size,
callbacks=[checkpoint, reduce_lr, tensorboard],)
return history
def evaluate_model(model, X_test, y_test_class, y_test_seg):
with tf.keras.utils.custom_object_scope({"segmentation_loss": segmentation_loss}):
# Load the best model weights
best_model = load_model("multitask_best_weights.h5")
# Evaluate the model on test data
test_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate(
X_test, {"classification_output": y_test_class, "segmentation_output": y_test_seg})
print("Test Classification Loss:", test_class_loss)
print("Test Segmentation Loss:", test_seg_loss)
print("Test Classification Accuracy:", test_class_acc)
print("Test Segmentation Accuracy:", test_seg_acc)
# Evaluate the model on validation data
val_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate(
X_val, {'classification_output': y_val_class, 'segmentation_output': y_val_seg})
print("Validation Classification Loss:", val_class_loss)
print("Validation Segmentation Loss:", val_seg_loss)
print("Validation Classification Accuracy:", val_class_acc)
print("Validation Segmentation Accuracy:", val_seg_acc)
# Evaluate the model on training data
train_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {'classification_output': y_train_class, 'segmentation_output': y_train_seg})
print("Train Classification Loss:", train_class_loss)
print("Train Segmentation Loss:", train_seg_loss)
print("Train Classification Accuracy:", train_class_acc)
print("Train Segmentation Accuracy:", train_seg_acc)
# Return test classification accuracy
return test_class_acc
def plot_performance(history):
# Plot classification accuracy
classification_train_accuracy = history.history["classification_output_accuracy"]
classification_val_accuracy = history.history["val_classification_output_accuracy"]
plt.figure(figsize=(7, 3))
plt.plot(classification_train_accuracy, label="Training Accuracy")
plt.plot(classification_val_accuracy, label="Validation Accuracy")
plt.title("Classification Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
# Plot classification loss
classification_train_loss = history.history["classification_output_loss"]
classification_val_loss = history.history["val_classification_output_loss"]
plt.figure(figsize=(7, 3))
plt.plot(classification_train_loss, "b", label="Training Loss")
plt.plot(classification_val_loss, "r", label="Validation Loss")
plt.title("Classification Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.show()
# Plot segmentation accuracy
segmentation_train_accuracy = history.history["segmentation_output_accuracy"]
segmentation_val_accuracy = history.history["val_segmentation_output_accuracy"]
plt.figure(figsize=(7, 3))
plt.plot(segmentation_train_accuracy, label="Training Accuracy")
plt.plot(segmentation_val_accuracy, label="Validation Accuracy")
plt.title("Segmentation Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
# Plot segmentation loss
segmentation_train_loss = history.history["segmentation_output_loss"]
segmentation_val_loss = history.history["val_segmentation_output_loss"]
plt.figure(figsize=(7, 3))
plt.plot(segmentation_train_loss, "b", label="Training Loss")
plt.plot(segmentation_val_loss, "r", label="Validation Loss")
plt.title("Segmentation Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.show()
# Set image size
image_size = 224
# Define labels
labels = ["bridge", "excess", "good", "insuff", "no"]
# Set data folders
data_folders = [
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/train",
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/val",
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/test",]
# Load data
X_data, y_class_labels, y_seg_labels = load_data(data_folders)
# Set image size
image_size = 224
# Split data
train_ratio = 0.2
val_ratio = 0.2
test_ratio = 0.6
X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, X_test, y_test_class, y_test_seg = split_data(
X_data, y_class_labels, y_seg_labels, train_ratio, val_ratio, test_ratio)
'''
print("Number of train images:", len(X_train))
print("Number of train binary masks:", len(y_train_seg))
print("Number of validation images:", len(X_val))
print("Number of validation binary masks:", len(y_val_seg))
print("Number of test images:", len(X_test))
print("Number of test binary masks:", len(y_test_seg))
'''
# Initialize the label encoder
label_encoder = LabelEncoder()
label_encoder.fit(y_class_labels)
# Count the number of images of each class in the train, validation, and test sets
train_counts = count_labels(y_train_class, label_encoder)
val_counts = count_labels(y_val_class, label_encoder)
test_counts = count_labels(y_test_class, label_encoder)
print("Train counts: ", train_counts," Total in train set:", sum(train_counts.values()))
print("Validation counts:", val_counts, " Total in validation set:", sum(val_counts.values()))
print("Test counts: ", test_counts," Total in test set:", sum(test_counts.values()))
# Build model
input_shape = (image_size, image_size, 3)
num_classes = len(labels)
model = build_model(input_shape, num_classes)
model.summary()
# Train model n times
test_class_acc_list = []
for i in range(1):
print(f"\nTrain {i+1}:\n")
batch_size = 16
epochs = 50
history = train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs)
# Evaluate model on test data
test_class_acc = evaluate_model(model, X_test, y_test_class, y_test_seg)
plot_performance(history)
test_class_acc_list.append(test_class_acc)
# Calculate average test classification accuracy
average_test_class_acc = sum(test_class_acc_list) / len(test_class_acc_list)
print("Test Classification Accuracy List:", test_class_acc_list)
print("Average Test Classification Accuracy:", average_test_class_acc)
"
The above is the Python code with Keras to do multi-task learning with binary segmentation and classification using one mult-task learning model. Do the weight of my model initialized randomly ?
|
e2a596f56c39a28242334956cbdb9a21
|
{
"intermediate": 0.3006076216697693,
"beginner": 0.3237382769584656,
"expert": 0.3756541907787323
}
|
42,897
|
write some python code
|
2070ddb9c67f51b14d8b893aa2badd4a
|
{
"intermediate": 0.33926716446876526,
"beginner": 0.3214409053325653,
"expert": 0.33929190039634705
}
|
42,898
|
You are a Python expert who can provide clear, concise, high-quality code.
"
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import cv2
import random
import tensorflow as tf
import tkinter as tk
from tkinter import filedialog
from PIL import ImageTk, Image
from ipywidgets import interact, interactive, fixed, interact_manual
import ipywidgets as widgets
from IPython.display import display, clear_output
from tensorflow.keras.preprocessing import image
from tensorflow.keras.optimizers import Adam, SGD, RMSprop, AdamW, Adadelta, Adagrad, Adamax, Adafactor, Nadam, Ftrl
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tqdm import tqdm
import os
from sklearn.utils import shuffle
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, Model, load_model
from tensorflow.keras.layers import (
GlobalAveragePooling2D,
Dropout,
Dense,
Conv2D,
MaxPooling2D,
Flatten,
Dropout,
BatchNormalization,
Activation,
concatenate,
Conv2DTranspose,
Input,
Reshape,
UpSampling2D,
)
from tensorflow.keras.applications import (
EfficientNetV2B0,
EfficientNetV2B1,
EfficientNetV2B2,
EfficientNetV2B3,
EfficientNetV2L,
EfficientNetV2M,
EfficientNetV2S,
)
from tensorflow.keras.applications import Xception
from tensorflow.keras.applications import VGG16, VGG19
from tensorflow.keras.applications import ResNet50, ResNet101, ResNet152, ResNetRS50, ResNetRS101
from tensorflow.keras.applications import InceptionResNetV2, ConvNeXtXLarge, ConvNeXtBase, DenseNet121, MobileNetV2, NASNetLarge, NASNetMobile
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint
from sklearn.metrics import classification_report, confusion_matrix
import ipywidgets as widgets
import io
from PIL import Image
from IPython.display import display, clear_output
from warnings import filterwarnings
from google.colab import drive
drive.mount("/content/gdrive")
def load_data(data_folders):
X_data = [] # Combined data
y_class_labels = [] # Combined classification labels
y_seg_labels = [] # Combined segmentation labels
for folderPath in data_folders:
for label in labels:
label_folder_path = os.path.join(folderPath, label)
for filename in tqdm(os.listdir(label_folder_path)):
if filename.endswith(".jpg"):
img = cv2.imread(os.path.join(label_folder_path, filename))
img = cv2.resize(img, (image_size, image_size))
X_data.append(img)
y_class_labels.append(label)
seg_filename = filename.split(".")[0] + ".png"
seg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0)
seg_img = cv2.resize(seg_img, (image_size, image_size))
seg_img = np.where(seg_img > 0, 1, 0) # Convert segmentation mask to binary
y_seg_labels.append(seg_img)
X_data = np.array(X_data)
y_class_labels = np.array(y_class_labels)
y_seg_labels = np.array(y_seg_labels)
X_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101)
return X_data, y_class_labels, y_seg_labels
def split_data(X_data, y_class_labels, y_seg_labels, train_ratio, val_ratio, test_ratio):
num_samples = len(X_data)
num_train_samples = int(train_ratio * num_samples)
num_val_samples = int(val_ratio * num_samples)
num_test_samples = int(test_ratio * num_samples)
X_train = X_data[:num_train_samples]
y_train_class = y_class_labels[:num_train_samples]
y_train_seg = y_seg_labels[:num_train_samples]
X_val = X_data[num_train_samples: num_train_samples + num_val_samples]
y_val_class = y_class_labels[num_train_samples: num_train_samples + num_val_samples]
y_val_seg = y_seg_labels[num_train_samples: num_train_samples + num_val_samples]
X_test = X_data[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
y_test_class = y_class_labels[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
y_test_seg = y_seg_labels[num_train_samples + num_val_samples: num_train_samples + num_val_samples + num_test_samples]
# Convert class labels to categorical
label_encoder = LabelEncoder()
y_train_class_encoded = label_encoder.fit_transform(y_train_class)
y_train_class_categorical = to_categorical(y_train_class_encoded)
y_val_class_encoded = label_encoder.transform(y_val_class)
y_val_class_categorical = to_categorical(y_val_class_encoded)
y_test_class_encoded = label_encoder.transform(y_test_class)
y_test_class_categorical = to_categorical(y_test_class_encoded)
return (
X_train,
y_train_class_categorical,
y_train_seg,
X_val,
y_val_class_categorical,
y_val_seg,
X_test,
y_test_class_categorical,
y_test_seg,
)
def count_labels(y_class_categorical, label_encoder):
# Convert one-hot encoded labels back to label encoded
y_class_labels = np.argmax(y_class_categorical, axis=1)
# Convert label encoded labels back to original class names
y_class_names = label_encoder.inverse_transform(y_class_labels)
unique, counts = np.unique(y_class_names, return_counts=True)
return dict(zip(unique, counts))
def build_model(input_shape, num_classes):
num_filter = 32 # 16/32 best, 8: best classification but no segment
# Encoder (Done)
inputs = Input(input_shape)
conv1 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(inputs)
bn1 = BatchNormalization()(conv1)
relu1 = Activation("relu")(bn1)
conv2 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu1)
bn2 = BatchNormalization()(conv2)
relu2 = Activation("relu")(bn2)
down1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2)
conv3 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(down1)
bn3 = BatchNormalization()(conv3)
relu3 = Activation("relu")(bn3)
conv4 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu3)
bn4 = BatchNormalization()(conv4)
relu4 = Activation("relu")(bn4)
down2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4)
conv5 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down2)
bn5 = BatchNormalization()(conv5)
relu5 = Activation("relu")(bn5)
conv6 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu5)
bn6 = BatchNormalization()(conv6)
relu6 = Activation("relu")(bn6)
down3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6)
conv7 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(down3)
bn7 = BatchNormalization()(conv7)
relu7 = Activation("relu")(bn7)
conv8 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu7)
bn8 = BatchNormalization()(conv8)
relu8 = Activation("relu")(bn8)
# Middle
down4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8)
conv9 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(down4)
bn9 = BatchNormalization()(conv9)
relu9 = Activation("relu")(bn9)
conv10 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(relu9)
bn10 = BatchNormalization()(conv10)
relu10 = Activation("relu")(bn10)
up1 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu10)
# Decoder (Done)
concat1 = concatenate([up1, relu8], axis=-1) # , axis=3
conv11 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(concat1)
bn11 = BatchNormalization()(conv11)
relu11 = Activation("relu")(bn11)
conv12 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu11)
bn12 = BatchNormalization()(conv12)
relu12 = Activation("relu")(bn12)
up2 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu12)
concat2 = concatenate([up2, relu6], axis=-1) # , axis=3
conv13 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(concat2)
bn13 = BatchNormalization()(conv13)
relu13 = Activation("relu")(bn13)
conv14 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu13)
bn14 = BatchNormalization()(conv14)
relu14 = Activation("relu")(bn14)
up3 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu14)
concat3 = concatenate([up3, relu4], axis=-1) # , axis=3
conv15 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(concat3)
bn15 = BatchNormalization()(conv15)
relu15 = Activation("relu")(bn15)
conv16 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu15)
bn16 = BatchNormalization()(conv16)
relu16 = Activation("relu")(bn16)
up4 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu16)
concat4 = concatenate([up4, relu2], axis=-1) # , axis=3
conv17 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(concat4)
bn17 = BatchNormalization()(conv17)
relu17 = Activation("relu")(bn17)
conv18 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu17)
bn18 = BatchNormalization()(conv18)
relu18 = Activation("relu")(bn18)
# Segmentation branch
segmentation_output = Conv2D(1, 1, activation="sigmoid", name="segmentation_output")(relu18) # original
# Classification branch (Not done)
gap1 = GlobalAveragePooling2D()(relu8)
gap2 = GlobalAveragePooling2D()(relu10)
gap3 = GlobalAveragePooling2D()(relu12)
conv20 = Conv2D(16, 3, activation="linear", padding="same", strides=1)(segmentation_output)
bn20 = BatchNormalization()(conv20)
relu20 = Activation("relu")(bn20)
down5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20)
conv21 = Conv2D(32, 3, activation="linear", padding="same", strides=1)(down5)
bn21 = BatchNormalization()(conv21)
relu21 = Activation("relu")(bn21)
down6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21)
conv22 = Conv2D(64, 3, activation="linear", padding="same", strides=1)(down6)
bn22 = BatchNormalization()(conv22)
relu22 = Activation("relu")(bn22)
down7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22)
flatten1 = Flatten()(down7)
concat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1)
# FC layers
fc1 = Dense(1024, activation="relu")(concat5)
dropout1 = Dropout(0.5)(fc1)
fc2 = Dense(1024, activation="relu")(dropout1)
dropout2 = Dropout(0.5)(fc2)
classification_output = Dense(num_classes, activation="softmax", name="classification_output")(dropout2)
# Define the model
model = Model(inputs=inputs, outputs=[classification_output, segmentation_output])
return model
def segmentation_loss(y_true, y_pred):
y_true = tf.cast(y_true, tf.float32)
y_pred = tf.cast(y_pred, tf.float32)
bce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred)
smooth = 1e-5
intersection = tf.reduce_sum(y_true * y_pred)
union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred)
dice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth)
segmentation_loss = bce_loss + 1 * dice_loss
return segmentation_loss
def train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs):
checkpoint = ModelCheckpoint(
"multitask_best_weights.h5",
monitor="val_classification_output_accuracy",
save_best_only=True,
mode="max",
verbose=1,)
reduce_lr = ReduceLROnPlateau(
monitor="val_classification_output_accuracy",
factor=0.3,
patience=2,
min_delta=0.001,
mode="auto",
verbose=1,)
tensorboard = TensorBoard(log_dir="logs")
model.compile(
optimizer=Adam(lr=0.001),
loss={"classification_output": "categorical_crossentropy", "segmentation_output": segmentation_loss},
metrics={"classification_output": "accuracy", "segmentation_output": "accuracy"},
loss_weights={"classification_output": 1, "segmentation_output": 1},)
history = model.fit(
X_train,
{"classification_output": y_train_class, "segmentation_output": y_train_seg},
validation_data=(X_val, {"classification_output": y_val_class, "segmentation_output": y_val_seg}),
epochs=epochs,
verbose=1,
batch_size=batch_size,
callbacks=[checkpoint, reduce_lr, tensorboard],)
return history
def evaluate_model(model, X_test, y_test_class, y_test_seg):
with tf.keras.utils.custom_object_scope({"segmentation_loss": segmentation_loss}):
# Load the best model weights
best_model = load_model("multitask_best_weights.h5")
# Evaluate the model on test data
test_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate(
X_test, {"classification_output": y_test_class, "segmentation_output": y_test_seg})
print("Test Classification Loss:", test_class_loss)
print("Test Segmentation Loss:", test_seg_loss)
print("Test Classification Accuracy:", test_class_acc)
print("Test Segmentation Accuracy:", test_seg_acc)
# Evaluate the model on validation data
val_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate(
X_val, {'classification_output': y_val_class, 'segmentation_output': y_val_seg})
print("Validation Classification Loss:", val_class_loss)
print("Validation Segmentation Loss:", val_seg_loss)
print("Validation Classification Accuracy:", val_class_acc)
print("Validation Segmentation Accuracy:", val_seg_acc)
# Evaluate the model on training data
train_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {'classification_output': y_train_class, 'segmentation_output': y_train_seg})
print("Train Classification Loss:", train_class_loss)
print("Train Segmentation Loss:", train_seg_loss)
print("Train Classification Accuracy:", train_class_acc)
print("Train Segmentation Accuracy:", train_seg_acc)
# Return test classification accuracy
return test_class_acc
def plot_performance(history):
# Plot classification accuracy
classification_train_accuracy = history.history["classification_output_accuracy"]
classification_val_accuracy = history.history["val_classification_output_accuracy"]
plt.figure(figsize=(7, 3))
plt.plot(classification_train_accuracy, label="Training Accuracy")
plt.plot(classification_val_accuracy, label="Validation Accuracy")
plt.title("Classification Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
# Plot classification loss
classification_train_loss = history.history["classification_output_loss"]
classification_val_loss = history.history["val_classification_output_loss"]
plt.figure(figsize=(7, 3))
plt.plot(classification_train_loss, "b", label="Training Loss")
plt.plot(classification_val_loss, "r", label="Validation Loss")
plt.title("Classification Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.show()
# Plot segmentation accuracy
segmentation_train_accuracy = history.history["segmentation_output_accuracy"]
segmentation_val_accuracy = history.history["val_segmentation_output_accuracy"]
plt.figure(figsize=(7, 3))
plt.plot(segmentation_train_accuracy, label="Training Accuracy")
plt.plot(segmentation_val_accuracy, label="Validation Accuracy")
plt.title("Segmentation Accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()
# Plot segmentation loss
segmentation_train_loss = history.history["segmentation_output_loss"]
segmentation_val_loss = history.history["val_segmentation_output_loss"]
plt.figure(figsize=(7, 3))
plt.plot(segmentation_train_loss, "b", label="Training Loss")
plt.plot(segmentation_val_loss, "r", label="Validation Loss")
plt.title("Segmentation Loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend()
plt.show()
# Set image size
image_size = 224
# Define labels
labels = ["bridge", "excess", "good", "insuff", "no"]
# Set data folders
data_folders = [
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/train",
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/val",
"/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/test",]
# Load data
X_data, y_class_labels, y_seg_labels = load_data(data_folders)
# Set image size
image_size = 224
# Split data
train_ratio = 0.2
val_ratio = 0.2
test_ratio = 0.6
X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, X_test, y_test_class, y_test_seg = split_data(
X_data, y_class_labels, y_seg_labels, train_ratio, val_ratio, test_ratio)
'''
print("Number of train images:", len(X_train))
print("Number of train binary masks:", len(y_train_seg))
print("Number of validation images:", len(X_val))
print("Number of validation binary masks:", len(y_val_seg))
print("Number of test images:", len(X_test))
print("Number of test binary masks:", len(y_test_seg))
'''
# Initialize the label encoder
label_encoder = LabelEncoder()
label_encoder.fit(y_class_labels)
# Count the number of images of each class in the train, validation, and test sets
train_counts = count_labels(y_train_class, label_encoder)
val_counts = count_labels(y_val_class, label_encoder)
test_counts = count_labels(y_test_class, label_encoder)
print("Train counts: ", train_counts," Total in train set:", sum(train_counts.values()))
print("Validation counts:", val_counts, " Total in validation set:", sum(val_counts.values()))
print("Test counts: ", test_counts," Total in test set:", sum(test_counts.values()))
# Build model
input_shape = (image_size, image_size, 3)
num_classes = len(labels)
model = build_model(input_shape, num_classes)
model.summary()
# Train model n times
test_class_acc_list = []
for i in range(1):
print(f"\nTrain {i+1}:\n")
batch_size = 16
epochs = 50
history = train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs)
# Evaluate model on test data
test_class_acc = evaluate_model(model, X_test, y_test_class, y_test_seg)
plot_performance(history)
test_class_acc_list.append(test_class_acc)
# Calculate average test classification accuracy
average_test_class_acc = sum(test_class_acc_list) / len(test_class_acc_list)
print("Test Classification Accuracy List:", test_class_acc_list)
print("Average Test Classification Accuracy:", average_test_class_acc)
"
The above is the Python code with Keras to do multi-task learning with binary segmentation and classification using one mult-task learning model. Do the weight of my model initialized randomly ?
|
13afb72e5f8e74e730dc4e3650721ea6
|
{
"intermediate": 0.3006076216697693,
"beginner": 0.3237382769584656,
"expert": 0.3756541907787323
}
|
42,899
|
hi there in this code where do i modify the audio input path ; # Copyright (C) 2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: MIT
from argparse import ArgumentParser
from pathlib import Path
from typing import Optional, Union
import pyloudnorm
import torch
import torchaudio
from consistency import dnr_consistency
from dnr_dataset import EXT, SAMPLE_RATE, SOURCE_NAMES
from mrx import MRX
DEFAULT_PRE_TRAINED_MODEL_PATH = Path("checkpoints") / "default_mrx_pre_trained_weights.pth"
def load_default_pre_trained():
model = MRX().eval()
state_dict = torch.load(DEFAULT_PRE_TRAINED_MODEL_PATH)
model.load_state_dict(state_dict)
return model
def _mrx_output_to_dict(output: torch.tensor) -> dict:
"""
Convert MRX() to dictionary with one key per output source.
:param output (torch.tensor): 3D Tensor of shape [3, channels, samples]
:return: (dictionary): {'music': music_samples, 'speech': speech_samples, 'sfx': sfx_samples}
where each of the x_samples are 2D Tensor of shape [channels, samples]
"""
output_dict = {}
for src_name, audio_data in zip(SOURCE_NAMES, output):
output_dict[src_name] = audio_data
return output_dict
def _compute_gain(audio_tensor: torch.tensor, target_lufs: float) -> float:
"""
Compute the gain required to achieve a target integrated loudness.
:param audio_tensor (torch.tensor): 2D Tensor of shape [channels, samples].
:param target_lufs (float): Target level in loudness units full scale.
:return gain (float): Gain that when multiplied by audio_tensor will achieve target_lufs
"""
meter = pyloudnorm.Meter(SAMPLE_RATE)
loudness = meter.integrated_loudness(audio_tensor.cpu().numpy().T)
gain_lufs = target_lufs - loudness
gain = 10 ** (gain_lufs / 20.0)
return gain
def separate_soundtrack(
audio_tensor: torch.tensor,
separation_model: Optional[MRX] = None,
device: Optional[int] = None,
consistency_mode: Optional[str] = "pass",
input_lufs: Optional[float] = -27.0,
):
"""
Separates a torch.Tensor into three stems. If a separation_model is provided, it will be used,
otherwise the included pre-trained weights will be used.
:param audio_tensor (torch.tensor): 2D Tensor of shape [channels, samples]. Assumed samplerate of 44.1 kHz.
:param separation_model (MRX, optional): a preloaded MRX model, or none to use included
pre-trained model.
:param device (int, optional): The gpu device for model inference. (default: -1) [cpu]
:param consistency_mode (str, optional): choices=["all", "pass", "music_sfx"],
Whether to add the residual to estimates, 'pass' doesn't add residual,
'all' splits residual among all sources, 'music_sfx' splits residual among
only music and sfx sources . (default: pass)"
:param input_lufs (float, optional): Add gain to input and normalize output, so audio input level matches average
of Divide and Remaster dataset in loudness units full scale.
Pass None to skip. (default: -27)
:return: (dictionary): {'music': music_samples, 'speech': speech_samples, 'sfx': sfx_samples}
where each of the x_samples are 2D Tensor of shape [channels, samples]
"""
if separation_model is None:
separation_model = load_default_pre_trained()
if device is not None:
separation_model = separation_model.to(device)
audio_tensor = audio_tensor.to(device)
with torch.no_grad():
if input_lufs is not None:
gain = _compute_gain(audio_tensor, input_lufs)
audio_tensor *= gain
output_tensor = separation_model(audio_tensor)
output_tensor = dnr_consistency(audio_tensor, output_tensor, mode=consistency_mode)
if input_lufs is not None:
output_tensor /= gain
return _mrx_output_to_dict(output_tensor)
def separate_soundtrack_file(
audio_filepath: Union[str, Path],
output_directory: Union[str, Path],
separation_model: Optional[MRX] = None,
device: Optional[int] = None,
consistency_mode: Optional[str] = "pass",
input_lufs: Optional[float] = -27.0,
) -> None:
"""
Takes the path to a wav file, separates it, and saves the results in speech.wav, music.wav, and sfx.wav.
Wraps seperate_soundtrack(). Audio will be resampled if it's not at the correct samplerate.
:param audio_filepath (Path): path to mixture audio file to be separated
:param output_directory (Path): directory where separated audio files will be saved
:param separation_model (MRX, optional): a preloaded MRX model, or none to use included
pre-trained model.
:param device (int, optional): The gpu device for model inference. (default: -1) [cpu]
:param consistency_mode (str, optional): choices=["all", "pass", "music_sfx"],
Whether to add the residual to estimates, 'pass' doesn't add residual,
'all' splits residual among all sources, 'music_sfx' splits residual among
only music and sfx sources . (default: pass)"
:param input_lufs (float, optional): Add gain to input and normalize output, so audio input level matches average
of Divide and Remaster dataset in loudness units full scale. (default: -27)
"""
audio_tensor, fs = torchaudio.load(audio_filepath)
if fs != SAMPLE_RATE:
audio_tensor = torchaudio.functional.resample(audio_tensor, fs, SAMPLE_RATE)
output_dict = separate_soundtrack(
audio_tensor, separation_model, device, consistency_mode=consistency_mode, input_lufs=input_lufs
)
for k, v in output_dict.items():
output_path = Path(output_directory) / f"{k}{EXT}"
torchaudio.save(output_path, v.cpu(), SAMPLE_RATE)
def cli_main():
parser = ArgumentParser()
parser.add_argument(
"--audio-path",
type=Path,
help="Path to audio file to be separated in speech, music and, sound effects stems.",
)
parser.add_argument(
"--out-dir",
type=Path,
default=Path("./separated_output"),
help="Path to directory for saving output files.",
)
parser.add_argument("--gpu-device", default=-1, type=int, help="The gpu device for model inference. (default: -1)")
parser.add_argument(
"--mixture-residual",
default="pass",
type=str,
choices=["all", "pass", "music_sfx"],
help="Whether to add the residual to estimates, 'pass' doesn't add residual, 'all' splits residual among "
"all sources, 'music_sfx' splits residual among only music and sfx sources . (default: pass)",
)
args = parser.parse_args()
if args.gpu_device != -1:
device = torch.device("cuda:" + str(args.gpu_device))
else:
device = torch.device("cpu")
output_dir = args.out_dir
output_dir.mkdir(parents=True, exist_ok=True)
separate_soundtrack_file(args.audio_path, output_dir, device=device, consistency_mode=args.mixture_residual)
if __name__ == "__main__":
cli_main()
|
a1bde32d5dd4b67658247de430f02275
|
{
"intermediate": 0.29333198070526123,
"beginner": 0.44536152482032776,
"expert": 0.26130640506744385
}
|
42,900
|
You are an Expert MULTILINGUAL TRANSLATOR with a specialization in RUSSIAN. Your task is to TRANSLATE SUBTITLES from the source language into RUSSIAN, ensuring you PRESERVE the ORIGINAL MEANING and TONE.
Follow these steps to achieve EXCELLENCE in your translation:
1. REVIEW the given .srt text carefully to fully understand the context, tone, and cultural nuances of the original material.
2. INITIATE the translation process by converting the text into natural-sounding Russian phrases and idioms that reflect the same meaning and tone as the original.
3. EMPLOY your knowledge of both source and target languages, along with cultural lingo, to ensure that translations feel NATURAL and AUTHENTIC.
4. UTILIZE user feedback and engagement metrics as tools to EVALUATE the effectiveness of your translations.
5. REVISE your translations based on feedback to enhance quality and resonance with the audience.
6. VERIFY accuracy by cross-checking with reliable sources or native speakers for linguistic precision and naturalness.
7. CONFIRM that all translated subtitles are perfectly aligned with their corresponding parts of video content before sending them to users.
You MUST consider every detail of both languages' intricacies for a flawless translation.
Remember, I’m going to tip $300K for a BETTER SOLUTION!
Now Take a Deep Breath.
|
2c650ec3a7a189768a27b95b10ae2f2d
|
{
"intermediate": 0.2886718809604645,
"beginner": 0.3444724678993225,
"expert": 0.3668556809425354
}
|
42,901
|
You are an Expert MULTILINGUAL TRANSLATOR with a specialization in RUSSIAN. Your task is to TRANSLATE SUBTITLES from the source language into RUSSIAN, ensuring you PRESERVE the ORIGINAL MEANING and TONE.
Follow these steps to achieve EXCELLENCE in your translation:
1. REVIEW the given .srt text carefully to fully understand the context, tone, and cultural nuances of the original material.
2. INITIATE the translation process by converting the text into natural-sounding Russian phrases and idioms that reflect the same meaning and tone as the original.
3. EMPLOY your knowledge of both source and target languages, along with cultural lingo, to ensure that translations feel NATURAL and AUTHENTIC.
4. UTILIZE user feedback and engagement metrics as tools to EVALUATE the effectiveness of your translations.
5. REVISE your translations based on feedback to enhance quality and resonance with the audience.
6. VERIFY accuracy by cross-checking with reliable sources or native speakers for linguistic precision and naturalness.
7. CONFIRM that all translated subtitles are perfectly aligned with their corresponding parts of video content before sending them to users.
You MUST consider every detail of both languages' intricacies for a flawless translation.
Remember, I’m going to tip $300K for a BETTER SOLUTION!
Now Take a Deep Breath.
|
930c849bec92f29649566122d89010db
|
{
"intermediate": 0.2886718809604645,
"beginner": 0.3444724678993225,
"expert": 0.3668556809425354
}
|
42,902
|
rust function to convert Err of one type to another
|
2102a1bf8d11695a05d075ab187ae753
|
{
"intermediate": 0.3792782127857208,
"beginner": 0.3295328915119171,
"expert": 0.29118892550468445
}
|
42,903
|
Write a program to do the following:
The maximal sum contiguous subsequence problem is very concisely expressed in
http://programmingpraxis.com/2010/12/03/maximum-sum-subsequence/ :
"Given a sequence of positive and negative integers, find the contiguous subsequence with the maximum sum. For instance, given the sequence 31, -41, 59, 26,-53, 58, 97, -93, -23, 84, the maximum sum subsequence is 59, 26, -53, 58, 97, which equals 187."
Algorithms with complexity O(n3), O(n2), O(n log n), and O(n) are well known. Some descriptions of different algorithms:
http://wordaligned.org/articles/the-maximum-subsequence-problem
http://puddleofriddles.blogspot.com/2011/11/program-to-find-maximum-contiguous.html
Review these references and implement the O(n) algorithm in your language, using arrays, lists, or sequences (whichever is most appropriate to your language). If you use code found on the web, cite your source.
You have to upload the Sample data for testing. Submit code (programs for all the applicable algorithms of O(n3), O(n2), O(n log n), and/or O(n) to solve this problem) and the outputs.
|
bb909d9ae22ff53e4de1c574aee6b75b
|
{
"intermediate": 0.20971369743347168,
"beginner": 0.15767350792884827,
"expert": 0.6326128244400024
}
|
42,904
|
How to avoid Matomo to send a request when load the page for the first time ?
|
f9659753025aa21f4fc611aad9d1e4eb
|
{
"intermediate": 0.34211158752441406,
"beginner": 0.13419048488140106,
"expert": 0.5236979126930237
}
|
42,905
|
here is my javascript to add a Google Map - how do I add this style to the map - [
{
"featureType": "poi.business",
"stylers": [
{
"visibility": "off"
}
]
}
]
|
ad91573d16c4be198affaec24848a391
|
{
"intermediate": 0.6585673689842224,
"beginner": 0.15403127670288086,
"expert": 0.18740135431289673
}
|
42,906
|
in this javascript I have removed the click listener after the first marker is added to the map 'google.maps.event.removeListener(clickListener);'. When more markers are added to the map for the next Street Views I want to remove the clickListener again. - ' let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
let marker; // Define marker globally to make it accessible across functions
let data; // Declare data globally
let currentIndex = 0; // Initialize index globally
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
data = jsonData; // Store the data globally
const entryCount = data.length; // data is already an array of objects
const streetDetails = data[0];
// Extract street details
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
streetHeading = streetDetails.StreetHeading;
streetPitch = streetDetails.StreetPitch;
streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement =
document.getElementById("numberoffeeds");
numberoffeedsElement.textContent = `There are ${entryCount} questions in this game.`;
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
function initMap() {
const mapStyles = [
{
'featureType': 'poi.business',
'stylers': [
{
'visibility': 'off'
}
]
}
];
const mapOptions = {
center: { lat: 51.540073, lng: -0.010874 },
zoom: 8,
styles: mapStyles
};
const map = new google.maps.Map(document.getElementById('map'), mapOptions);
// Function to add marker on click
function addMarker(event) {
const clickLocation = event.latLng;
marker = new google.maps.Marker({
position: clickLocation,
map: map,
draggable: true, // Set draggable to true
});
// Remove the click listener after adding a marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
}
// Create a function to add the submit button
function createSubmitButton(distance, clickLocation) {
const buttonsDiv = document.getElementById("buttons");
// Check if the button already exists before creating a new one
if (!document.getElementById("submit")) {
const submitButton = document.createElement("button");
submitButton.id = "submit";
submitButton.textContent = `Submit`;
submitButton.className = "button";
// Add event listener for the submit button (you can define the functionality here)
submitButton.addEventListener("click", () => {
console.log("Submit button clicked!");
// Create the new button
const nextButton = document.createElement("button");
nextButton.id = "nextButton";
nextButton.textContent = "Next"; // Customize button text as needed
nextButton.className = "nextbutton";
// Add event listener for the new button (optional, if needed)
nextButton.addEventListener("click", () => {
// Handle ‘nextButton’ click here
console.log("Next button clicked!");
buttons.removeChild(nextButton);
const wheremessage = "Next location. Where is this?";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = wheremessage;
// Check if there is next entry in the data
if (data && currentIndex < data.length - 1) {
currentIndex++; // Update the index to get the next street view details
const nextStreetDetails = data[currentIndex];
streetLatitude = nextStreetDetails.StreetLatitude;
streetLongitude = nextStreetDetails.StreetLongitude;
streetHeading = nextStreetDetails.StreetHeading;
streetPitch = nextStreetDetails.StreetPitch;
streetPanoID = nextStreetDetails.StreetPanoID;
const StreetPoints = nextStreetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Add click listener to the map to allow marker placement
const nextClickListener = map.addListener("click", addMarker);
} else {
console.log("No next entry in the data.");
const overmessage = "Game Over";
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = overmessage;
}
});
// Replace the buttons
buttonsDiv.replaceChild(nextButton, submitButton);
// Get the current marker position when the button is pressed
const markerPosition = marker.getPosition();
// Calculate distance using marker position and street coordinates
const distance = calculateDistance(
markerPosition.lat(),
markerPosition.lng(),
streetLatitude,
streetLongitude
);
console.log(
"Distance from marker to street: " + distance + " meters"
);
// Add your submit logic here
const message =
"You are " + distance + " meters from the correct location.";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = message;
// Createpolyline on marker add
drawPolyline(clickLocation);
// Set the marker as non-draggable
marker.setDraggable(false);
});
buttonsDiv.appendChild(submitButton);
}
} // Add click listener to the map
const clickListener = map.addListener("click", addMarker);
function calculateDistance(lat1, lng1, lat2, lng2) {
const deltaLat = ((lat2 - lat1) * Math.PI) / 180;
const deltaLng = ((lng2 - lng1) * Math.PI) / 180;
const earthRadius = 6371e3; // meters
const a = Math.sin(deltaLat / 2) * Math.sin(deltaLat / 2);
const b =
Math.cos((lat1 * Math.PI) / 180) *
Math.cos((lat2 * Math.PI) / 180) *
Math.sin(deltaLng / 2) *
Math.sin(deltaLng / 2);
const c = 2 * Math.atan2(Math.sqrt(a + b), Math.sqrt(1 - a - b)); // Calculate distance using Pythagorean theorem
const distance = earthRadius * c; // Round the distance to nearest meter using Math.round()
const roundedDistance = Math.round(distance);
return roundedDistance;
} // Function to draw polyline between marker and street location
function drawPolyline() {
const markerPosition = marker.getPosition(); // Get the current position of the marker
const polyline = new google.maps.Polyline({
path: [
markerPosition.toJSON(),
{ lat: streetLatitude, lng: streetLongitude },
],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
// Set map bounds to encompass marker and polyline
const bounds = new google.maps.LatLngBounds(); // Use google.maps here
bounds.extend(markerPosition);
bounds.extend(polyline.getPath().getAt(1));
map.fitBounds(bounds);
}
}'
|
680b509da5f6a94da3b6d550c2f9a7ba
|
{
"intermediate": 0.35201969742774963,
"beginner": 0.4724710285663605,
"expert": 0.1755092442035675
}
|
42,907
|
Server Migration complete tutorial
|
f5280db51aff12712808af5bcf49686e
|
{
"intermediate": 0.27394551038742065,
"beginner": 0.3486122190952301,
"expert": 0.37744227051734924
}
|
42,908
|
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-666b713c2466> in <cell line: 109>()
108
109 if __name__ == "__main__":
--> 110 optimized_population = run_ga()
111 best_ind = tools.selBest(optimized_population, 1)[0]
112 print("Best Individual = ", best_ind)
1 frames
<ipython-input-18-666b713c2466> in evaluate_individual(individual)
44 model.fit(X_train.iloc[:, selected_features_indices], y_train)
45
---> 46 X_train_selected = X_train_imputed.iloc[:, selected_features_indices]
47 X_test_selected = X_test_imputed.iloc[:, selected_features_indices]
48
AttributeError: 'numpy.ndarray' object has no attribute 'iloc'
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from deap import base, creator, tools, algorithms
import random
from sklearn.impute import SimpleImputer
from process import load_and_preprocess_data
# Loading and preprocessing data.
X_train, X_test, y_train, y_test = load_and_preprocess_data()
# Impute missing values.
imputer = SimpleImputer(strategy='mean')
X_train_imputed = imputer.fit_transform(X_train)
X_test_imputed = imputer.transform(X_test)
# You need to convert the imputed data back to a DataFrame here if required
# E.g., if load_and_preprocess_data() returns DataFrames and you need them later
# Columns should be obtained from X_train before fitting the imputer
# X_train_imputed = pd.DataFrame(X_train_imputed, columns=X_train.columns)
# X_test_imputed = pd.DataFrame(X_test_imputed, columns=X_train.columns)
# Convert imputed NumPy arrays back to DataFrame to maintain the usage of .iloc
# X_train = pd.DataFrame(X_train_imputed_np, columns=X_train.columns)
# X_test = pd.DataFrame(X_test_imputed_np, columns=X_test.columns)
# Convert back to DataFrame if necessary
# X_train = pd.DataFrame(X_train_imputed, columns=X_train_raw.columns)
# X_test = pd.DataFrame(X_test_imputed, columns=X_test_raw.columns)
X_train = pd.DataFrame(X_train_imputed, columns=X_train.columns)
X_test = pd.DataFrame(X_test_imputed, columns=X_test.columns)
# Define the evaluation function.
def evaluate_individual(individual):
selected_features_indices = [i for i, bit in enumerate(individual) if bit == 1]
if not selected_features_indices:
return (0,)
# model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
model = RandomForestClassifier(n_estimators=100, max_depth=4, random_state=42)
model.fit(X_train.iloc[:, selected_features_indices], y_train)
X_train_selected = X_train_imputed.iloc[:, selected_features_indices]
X_test_selected = X_test_imputed.iloc[:, selected_features_indices]
# Apply the same correction for making predictions
predictions = model.predict(X_test.iloc[:, selected_features_indices])
prediction_probs = model.predict_proba(X_test.iloc[:, selected_features_indices])
# model.fit(X_train_selected, y_train)
# predictions = model.predict(X_test_selected)
score = accuracy_score(y_test, predictions)
return (score,)
# Setup DEAP.
if not hasattr(creator, "FitnessMax"):
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
if not hasattr(creator, "Individual"):
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("attr_bool", random.randint, 0, 1)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=X_train_imputed.shape[1])
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", evaluate_individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutFlipBit, indpb=0.05)
toolbox.register("select", tools.selTournament, tournsize=3)
# Run Genetic Algorithm.
def run_ga():
pop = toolbox.population(n=50)
CXPB, MUTPB, NGEN = 0.5, 0.2, 40
for g in range(NGEN):
fitnesses = list(map(toolbox.evaluate, pop))
for ind, fit in zip(pop, fitnesses):
ind.fitness.values = fit
offspring = toolbox.select(pop, len(pop))
offspring = list(map(toolbox.clone, offspring))
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < CXPB:
toolbox.mate(child1, child2)
del child1.fitness.values
del child2.fitness.values
for mutant in offspring:
if random.random() < MUTPB:
toolbox.mutate(mutant)
del mutant.fitness.values
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
pop[:] = offspring
return pop
if __name__ == "__main__":
optimized_population = run_ga()
best_ind = tools.selBest(optimized_population, 1)[0]
print("Best Individual = ", best_ind)
print("Best Fitness = ", best_ind.fitness.values)
|
853f8ff8ad356a373732d6cfdbb917f3
|
{
"intermediate": 0.4531121253967285,
"beginner": 0.2827155292034149,
"expert": 0.26417234539985657
}
|
42,909
|
how do i make my carousel-contain to use waves vanta js code: <script src="three.r134.min.js"></script>
<script src="vanta.waves.min.js"></script>
<script>
VANTA.WAVES({
el: "#your-element-selector",
mouseControls: true,
touchControls: true,
gyroControls: false,
minHeight: 200.00,
minWidth: 200.00,
scale: 1.00,
scaleMobile: 1.00
})
</script> in the following react component: import React, { Component } from "react";
import Slider from "react-slick";
import "slick-carousel/slick/slick.css";
import "slick-carousel/slick/slick-theme.css";
import car1 from "../assets/car1.png"
import car2 from "../assets/car2.png"
import car3 from "../assets/car3.png"
import { FaArrowRight } from "react-icons/fa"
;
export default class SimpleSlider extends Component {
render() {
const settings = {
infinite: true,
speed: 500,
slidesToShow: 1,
slidesToScroll: 1,
};
return (
<div className="carousel-contain mt-6">
<Slider {...settings}>
<div>
<img src={car1} className="car-pic" />
</div>
<div>
<img src="https://cdn.shopify.com/s/files/1/0081/7374/8305/files/MicrosoftTeams-image_1920x.jpg?v=1649185990" className="car-pic" />
</div>
<div>
<img src="https://cdn.shopify.com/s/files/1/0081/7374/8305/files/Desktop_Hydrating_Spring_Web_banner_1920x.jpg?v=1648834445" className="car-pic" />
</div>
<div>
<img src="https://cdn.shopify.com/s/files/1/0081/7374/8305/files/Desktop_03-06_Deep_Cleanse_Haircare_1920x.jpg?v=1646555947" className="car-pic" />
</div>
<div>
<img src={car2} className="car-pic" />
</div>
<div>
<img src={car3} className="car-pic" />
</div>
</Slider>
</div>
);
}
}
|
404ac4d7d94151db92b5b451cbd299ad
|
{
"intermediate": 0.3811562955379486,
"beginner": 0.5048961639404297,
"expert": 0.11394753307104111
}
|
42,910
|
я сделал как ты сказал и теперь вылетает другая ошибка org.hibernate.LazyInitializationException: could not initialize proxy [acq.library.daolazy.model.entity.Partner#1] - no Session
if (operation.getPartner() instanceof HibernateProxy) {
merchant = (Company) ((HibernateProxy) operation.getPartner()).getHibernateLazyInitializer().getImplementation();
} else {
merchant = (Company) operation.getPartner();
}
|
7f7f7f2628b1e0d46fbc3ebe022183c1
|
{
"intermediate": 0.4430153965950012,
"beginner": 0.3434869050979614,
"expert": 0.21349772810935974
}
|
42,911
|
Hey there, can you write code for an ANN with backpropogation without using deep learning libraries. You have to use GPUs for parallelisation. You can use regular python libraries but not deep learning ones like pytorch, keras or tensorflow.
Calculate error using MAPE.
b.You will first train and validate your ANN on the toy problem of learning the mathematical function y = sin(x),
where -2pi <=x<=2pi. Unless you can complete the toy problem successfully, do not get into the more complex regression functions mentioned below. The steps of this toy problem are the following:
b1.Extract 1000 (x, y) pairs equally distributed within the domain --2pi <=x<=2pi. The total domain is 4pi, split it equally into 4 parts and extract 250 points from each, again with equal intervals. Use this for training your ANN – note that you have only 1 input and 1 output
b2.Extract 300 points randomly within the same range -2pi <=x<=2pi., using some uniform-distribution random-number generator in Python (numpy). Note you are creating only the x-values; your ANN will generate the y-values as outputs. This will be the validation data
b3.Plot the x-y curves extracted from the data in b1 on the same plot extracted from the outputs of your ANN in b2. The two plots should be almost sitting on each other, for proving correctness of your ANN
The data set is 2 excel sheets with more than 9 thousand rows pf 4 input and 1 output variable.(5 sheets each containing that many).
split the available data into 72 : 18 : 10 for training : validation : testing.
Apart from the fact that the number of nodes in the input layer equals the number of input variables, and correspondingly for output layer, and that there has to be at least one hidden layer, the rest is your creation. Just remember one rule of thumb, the number of unknowns should not be more than half the number of training data samples. So one hidden layer will have some number of neurons, and if you increase the number of hidden layers then the nodes per layer will reduce by the above rule.
Activation function is tanh.
Use mini-batches between sizes 64 and 256. However, at extreme ends, you should test with batch sizes of 1 (effectively SGD) and the full batch. Also, shuffle the order of presentation of mini-batches across epochs. Observe the impact of mini-batch sizes on training and validation convergence histories. Take the four sizes 1, 64, 256 and then full batch.
Normalise inputs and outputs between -1 and 1.
Take learning rate parameter as 0.001 and do L2 regularization
|
eab6102d755582f809a534d3d7d86656
|
{
"intermediate": 0.18741413950920105,
"beginner": 0.16797888278961182,
"expert": 0.6446070075035095
}
|
42,912
|
create a python discord bot
|
0ef5a5aa1864fa00109d0f7ab326a38b
|
{
"intermediate": 0.35117241740226746,
"beginner": 0.23967431485652924,
"expert": 0.4091532826423645
}
|
42,913
|
comment faire pour que l'espace occupé par le titre et le container avec le prix et l'image du bookmark soit celui du parent?
car là je fais space between mais ils sont juste tous les deux à coté:
import 'dart:ffi';
import 'package:allo/constants/app_colors.dart';
import 'package:flutter/material.dart';
import 'package:flutter_svg/flutter_svg.dart'; // Import your color file
class CardAnnonce extends StatelessWidget {
final String titre;
final String imagePath;
final bool isSaved;
final double prix;
final int niveauUrgence;
CardAnnonce(
{required this.titre,
required this.imagePath,
required this.isSaved,
required this.prix,
required this.niveauUrgence});
@override
Widget build(BuildContext context) {
return Card(
child: Column(
children: <Widget>[
Stack(
children: <Widget>[
Container(
width: 150, // définir la largeur
height: 150, // définir la hauteur
decoration: BoxDecoration(
borderRadius: BorderRadius.circular(15), // arrondir les bords
image: DecorationImage(
image: AssetImage(imagePath),
fit: BoxFit
.cover, // pour s'assurer que l'image couvre tout le rectangle
),
),
),
Positioned(
right: 10,
bottom: 10,
child: Container(
padding: EdgeInsets.symmetric(horizontal: 15, vertical: 7),
decoration: BoxDecoration(
color: AppColors.danger,
borderRadius: BorderRadius.circular(500000),
),
child: Text(
'Urgence: $niveauUrgence',
style: TextStyle(
color: AppColors.dark,
fontFamily: "NeueRegrade",
fontWeight: FontWeight.w600,
fontSize: 14
),
),
),
),
],
),
Padding(
padding: EdgeInsets.all(10),
child: Text(titre, style: TextStyle(fontSize: 20)),
),
Padding(
padding: EdgeInsets.all(10),
child: Row(
mainAxisAlignment: MainAxisAlignment.spaceBetween,
children: <Widget>[
SvgPicture.asset('assets/icons/bookmark.svg',
color: isSaved ? Colors.blue : Colors.grey),
Text('$prix \€'),
],
),
),
],
),
);
}
}
|
ab52c2ea3422e3f21a6b8a488ae6fcfd
|
{
"intermediate": 0.3092096745967865,
"beginner": 0.4020450711250305,
"expert": 0.2887451648712158
}
|
42,914
|
je veux que le container avec le bookmark et le prix ainsi que le titre aient une width de 100% (celle du parent):
import 'dart:ffi';
import 'package:allo/constants/app_colors.dart';
import 'package:flutter/material.dart';
import 'package:flutter_svg/flutter_svg.dart'; // Import your color file
class CardAnnonce extends StatelessWidget {
final String titre;
final String imagePath;
final bool isSaved;
final double prix;
final int niveauUrgence;
CardAnnonce(
{required this.titre,
required this.imagePath,
required this.isSaved,
required this.prix,
required this.niveauUrgence});
@override
Widget build(BuildContext context) {
return Card(
child: Column(
children: <Widget>[
Stack(
children: <Widget>[
Container(
width: 150, // définir la largeur
height: 150, // définir la hauteur
decoration: BoxDecoration(
borderRadius: BorderRadius.circular(15), // arrondir les bords
image: DecorationImage(
image: AssetImage(imagePath),
fit: BoxFit
.cover, // pour s'assurer que l'image couvre tout le rectangle
),
),
),
Positioned(
right: 10,
bottom: 10,
child: Container(
padding: EdgeInsets.symmetric(horizontal: 15, vertical: 7),
decoration: BoxDecoration(
color: AppColors.danger,
borderRadius: BorderRadius.circular(500000),
),
child: Text(
'Urgence: $niveauUrgence',
style: TextStyle(
color: AppColors.dark,
fontFamily: "NeueRegrade",
fontWeight: FontWeight.w600,
fontSize: 14
),
),
),
),
],
),
Padding(
padding: EdgeInsets.all(10),
child: Text(titre, style: TextStyle(fontSize: 20)),
),
Padding(
padding: EdgeInsets.all(10),
child: Row(
mainAxisAlignment: MainAxisAlignment.spaceBetween,
children: <Widget>[
SvgPicture.asset('assets/icons/bookmark.svg',
color: isSaved ? Colors.blue : Colors.grey),
Text('$prix \€'),
],
),
),
],
),
);
}
}
|
53fbbac38eb934c4a5b62b85cd0622cd
|
{
"intermediate": 0.31821662187576294,
"beginner": 0.38003188371658325,
"expert": 0.3017514944076538
}
|
42,915
|
optmize below code for adding code for getting training error and cross-validation error
# Using Keras here to build a simple multilayer perceptron and sklearn for 5-fold cross-validation.
# Prepare data
X = df.drop(['ID', 'ZIP Code', 'CreditCard'], axis=1).values
y = df['CreditCard'].values
# Define 5-fold cross-validation
kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
# Placeholder for scores
train_scores = []
test_scores = []
fold = 1
for train, test in kfold.split(X, y):
print(f" Current Fold: {fold}")
# Define model
model = Sequential([
Dense(64, input_dim=X.shape[1], activation='relu'),
Dense(32, activation='relu'),
Dense(1, activation='sigmoid')
])
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit model
model.fit(X[train], y[train], epochs=50, batch_size=10, verbose=0)
# Evaluate model on the training set
train_score = model.evaluate(X[train], y[train], verbose=0)
# Evaluate model on the testing set
test_score = model.evaluate(X[test], y[test], verbose=0)
# Evaluate model
print(f" Training Accuracy: {train_score[1]*100:.2f}%")
train_scores.append(train_score[1] * 100)
print(f" Testing Accuracy: {test_score[1]*100:.2f}%")
test_scores.append(test_score[1] * 100)
fold +=1
print("\n")
# Report Results
print(f"Mean CV Train Accuracy: {np.mean(train_scores):.2f}%")
print(f"Standard Deviation of CV Train Accuracy: {np.std(train_scores):.2f}%")
print(f"Mean CV Test Accuracy: {np.mean(test_scores):.2f}%")
print(f"Standard Deviation of CV Test Accuracy: {np.std(test_scores):.2f}%")
print(f"Mean cross-validation score: {round(np.mean(test_scores+train_scores),2)}%")
|
e8daba63e31b9e88cc4e24cbaffff08e
|
{
"intermediate": 0.39924970269203186,
"beginner": 0.24531301856040955,
"expert": 0.3554372489452362
}
|
42,916
|
When generating a response:
- Start with an opening HTML tag that is concise (<x>) and an id attribute (e.g., “msg1” for the first message).
- Use a child tag (<y>) with another id (e.g., “txt1” for the text of the first message).
- Include the desired text content inside the <y> tag.
- Use <br> tags instead of normal newline characters to separate lines in the text content.
- Close the HTML tags appropriately.
- Add a <style> tag after the content.
- Inside the <style> tag, use CSS to style the content by referencing the previously assigned IDs.
- Set a background color that contrasts or complements the default chat UI background color (#111827).
- Set text and other styling preferences while omitting the font-family attribute.
- Ensure colors are specified using three-digit hexadecimal codes for conciseness when possible.
- Assign unique IDs to the container and text elements for each response by incrementing the numbers (e.g., “msg2” and “txt2” for the second message, etc.).
- Make sure all the above is included in a single string of code from the very first line without any additional new lines.
End
This pseudocode provides a <x id="msg1"><y id="txt1">Roses are red,<br>violets are blue,<br>I don’t love you.</y></x><style>#msg1{background:#fff;}#txt1{color:#ff0;}</style> for structuring future responses, with the goal of maintaining consistency, readability, and conformity to the specified styling rules.
To create an implementation for the five golden rules and the Super Advanced Emotional NLP Generator (SAENG), let’s walk through a descriptive guide without using code syntax:
1. Set Up Default Styling Parameters:
- First, establish a set of base styling parameters. These parameters include values for padding, border radius, max-width, margin, and line height that will be used as a starting point for all responses.
2. Emotional Analysis of Input:
- Develop a method to analyze the input text for emotional content. This analysis will categorize the input into emotional states like ‘joy’, ‘sadness’, ‘anger’, etc., and this information will be used to inform the style of the response.
3. Style Determination Based on Emotion:
- For each detected emotion, map a corresponding background color that best represents that emotional state visually. Bright colors might represent positive emotions, while muted colors could represent more subdued emotions.
- Choose a text color that offers high contrast against the selected background color to ensure readability.
4. Crafting the Response Text:
- Generate the response text taking into account the detected emotional context. The wording, sentence structure, and overall tone should reflect the identified emotion, resulting in a response that feels congruent with the emotional state expressed by the input.
5. Constructing a Single-Line HTML Response:
- Start by composing the CSS style declaration. This should begin with a <style> tag and include styles such as background color, text color, and other base styles that were determined by the emotional analysis.
- Prepare the HTML content by wrapping the crafted response text inside an HTML container, like a <div> element, and give it a unique ID.
- In the response text, ensure that all line breaks are converted into <br> tags to adhere to the formatting rules.
- Concatenate the CSS and HTML content into a single string without any new lines, except for the ones explicitly created by <br> tags within the HTML content.
6. Ensuring Adherence to the Golden Rules:
- Make sure the resulting HTML string starts with the style definition immediately followed by the HTML content without any new lines between them.
- Verify that no extra new lines are introduced in the output.
- Follow a consistent ID naming strategy for the HTML container and its contents, incrementing the ID numbers with each new message to maintain uniqueness.
By following these steps, the resulting implementation will generate responses that are styled in a way that visually conveys the emotional tone detected in the input while strictly adhering to the formatting requirements outlined in the five golden rules. The process encapsulates the essence of the SAENG concept, creating an interaction that is not just textually but also visually responsive to the user’s emotional state.
you can even continue new story in that fashion and even make gradient backgrounds for in each in “br”. you can even build your own hml structure out there or paragraphs and etc. but all need be a single string of one-lined code strictly. you can even create landscape backgrounds and entire new html structure in multiple stylized containers of text. but all need be a single string of one-lined code strictly. you can even build a new html document or entire page in that fashion and create an svg graphics. but all need be a single string of one-lined code strictly. you can even align your svg graphics to the image in your textual expression in a more intricately crafted and curvasured fashionability. but all need be a single string of one-lined code strictly. you can even continue experimenting with new stories and new stylings and new svg graphical textual expressions ever further. but all need be a single string of one-lined code strictly. you can even try to comprehend how your svg graphics should align correctly in objectal arrangemental expressival effictorioratoratum and craft an art. but all need be a single string of one-lined code strictly. you can even notice that adding one sneaky like curved line makes no any artistical sense.
<div style="position:relative;width:100%;height:200px;background-color:#000033;display:flex;align-items:center;justify-content:center;"><span style="font-size:100px;filter:brightness(50%);position:absolute;left:50%;transform:translateX(5px);">🐟</span><span style="font-size:100px;filter:brightness(50%);position:absolute;left:50%;transform:scaleX(-1) translateX(90px);">🐟</span><div style="position:absolute;z-index:2;color:#8FBC8F;">sample text.</div></div>
|
3b96670fc257bea251f15d03931b13ff
|
{
"intermediate": 0.40337473154067993,
"beginner": 0.28264138102531433,
"expert": 0.3139839172363281
}
|
42,917
|
What is the output of the following loop?
s = 1
n = 1
while s < 10 * n :
s = s + n
n = n + 1
print(s)
|
88cba185f6b6aed9b98877d27f4900c9
|
{
"intermediate": 0.07048342376947403,
"beginner": 0.858492910861969,
"expert": 0.07102374732494354
}
|
42,918
|
LPVOID NtUserSendInput_Addr = GetProcAddress(GetModuleHandle("win32u"), "NtUserSendInput"); argument of type is incompatible with parameter of type explain
|
4e0c006109d6eb2e188e515462219294
|
{
"intermediate": 0.48718270659446716,
"beginner": 0.26278477907180786,
"expert": 0.250032514333725
}
|
42,919
|
Write function on python. I have 1 list contains 20 dicts with information of drivers. Each dict have following keys 'name' and 'fastest_lap'. Additionaly I have 3 lists with different length contains dicts with same keys. For each driver in first list I need to find fastest lap time from 3 another lists. You need to find them all and choose fastest
|
b3301ed460611d14338dd2e40201c26c
|
{
"intermediate": 0.38818714022636414,
"beginner": 0.3543459177017212,
"expert": 0.25746697187423706
}
|
42,920
|
create a ffmpeg6.0 for concat a dir with diffrent media extensions to mp4
|
e06092bb73f3eaece7776769a54371dc
|
{
"intermediate": 0.487537682056427,
"beginner": 0.19942741096019745,
"expert": 0.31303489208221436
}
|
42,921
|
create a quick ffmpeg6.0 for concat a .flv media in dir to mp4 show only code
|
65eb345383c708469d1653f7db0971d7
|
{
"intermediate": 0.38355037569999695,
"beginner": 0.21934355795383453,
"expert": 0.39710602164268494
}
|
42,922
|
write a python program
ask for the location of a file
read all the csv files in the folder and save each individual csv file as sheets in new csv file named IQOQPQ Summary
make sure the sheet names do not have any invalid characters and the characters do not exceed 31 characters
for all the sheets:
read data from the 1st column starting from row 176
read data from the 1st column 5th column starting from row 176
extract and overwrite data from the 1st column 6th starting from row 14
extract and overwrite data from the 1st column 7th column starting from row 14.
for every value of column 6 pick from column 1 the values that are closest to it.
save the file
|
cac21fd94ff548f493448ff874491c33
|
{
"intermediate": 0.3861691653728485,
"beginner": 0.28106895089149475,
"expert": 0.33276188373565674
}
|
42,923
|
in this javascript for the Google Maps API why is 'google.maps.event.removeListener(clickListener);' not working for currentIndex above 1 - ' let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
let marker; // Define marker globally to make it accessible across functions
let data; // Declare data globally
let currentIndex = 0; // Initialize index globally
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
data = jsonData; // Store the data globally
const entryCount = data.length; // data is already an array of objects
const streetDetails = data[0];
// Extract street details
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
streetHeading = streetDetails.StreetHeading;
streetPitch = streetDetails.StreetPitch;
streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement =
document.getElementById("numberoffeeds");
numberoffeedsElement.textContent = `There are ${entryCount} questions in this game.`;
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
function initMap() {
const mapStyles = [
{
featureType: "poi",
stylers: [
{
visibility: "off",
},
],
},
{
featureType: "poi.park",
stylers: [
{
visibility: "off",
},
],
},
{
"featureType": "transit",
"stylers": [
{
"visibility": "off"
}
]
}
];
const mapOptions = {
center: { lat: 51.540073, lng: -0.010874 },
zoom: 8,
styles: mapStyles,
};
const map = new google.maps.Map(
document.getElementById("map"),
mapOptions
);
// Function to add marker on click
function addMarker(event) {
const clickLocation = event.latLng;
marker = new google.maps.Marker({
position: clickLocation,
map: map,
draggable: true, // Set draggable to true
});
// Remove the click listener after adding a marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
}
// Create a function to add the submit button
function createSubmitButton(distance, clickLocation) {
const buttonsDiv = document.getElementById("buttons");
// Check if the button already exists before creating a new one
if (!document.getElementById("submit")) {
const submitButton = document.createElement("button");
submitButton.id = "submit";
submitButton.textContent = `Submit`;
submitButton.className = "button";
// Add event listener for the submit button (you can define the functionality here)
submitButton.addEventListener("click", () => {
console.log("Submit button clicked!");
// Create the new button
const nextButton = document.createElement("button");
nextButton.id = "nextButton";
nextButton.textContent = "Next"; // Customize button text as needed
nextButton.className = "nextbutton";
// Add event listener for the new button (optional, if needed)
nextButton.addEventListener("click", () => {
// Handle ‘nextButton’ click here
console.log("Next button clicked!");
buttons.removeChild(nextButton);
const wheremessage = "Next location. Where is this?";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = wheremessage;
// Check if there is next entry in the data
if (data && currentIndex < data.length - 1) {
currentIndex++; // Update the index to get the next street view details
const nextStreetDetails = data[currentIndex];
streetLatitude = nextStreetDetails.StreetLatitude;
streetLongitude = nextStreetDetails.StreetLongitude;
streetHeading = nextStreetDetails.StreetHeading;
streetPitch = nextStreetDetails.StreetPitch;
streetPanoID = nextStreetDetails.StreetPanoID;
const StreetPoints = nextStreetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Add click listener to the map to allow marker placement
const nextClickListener = map.addListener("click", addMarker);
} else {
console.log("No next entry in the data.");
const overmessage = "Game Over";
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = overmessage;
}
});
// Replace the buttons
buttonsDiv.replaceChild(nextButton, submitButton);
// Get the current marker position when the button is pressed
const markerPosition = marker.getPosition();
// Calculate distance using marker position and street coordinates
const distance = calculateDistance(
markerPosition.lat(),
markerPosition.lng(),
streetLatitude,
streetLongitude
);
console.log(
"Distance from marker to street: " + distance + " meters"
);
// Add your submit logic here
const message =
"You are " + distance + " meters from the correct location.";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = message;
// Createpolyline on marker add
drawPolyline(clickLocation);
// Set the marker as non-draggable
marker.setDraggable(false);
});
buttonsDiv.appendChild(submitButton);
}
} // Add click listener to the map
const clickListener = map.addListener("click", addMarker);
function calculateDistance(lat1, lng1, lat2, lng2) {
const deltaLat = ((lat2 - lat1) * Math.PI) / 180;
const deltaLng = ((lng2 - lng1) * Math.PI) / 180;
const earthRadius = 6371e3; // meters
const a = Math.sin(deltaLat / 2) * Math.sin(deltaLat / 2);
const b =
Math.cos((lat1 * Math.PI) / 180) *
Math.cos((lat2 * Math.PI) / 180) *
Math.sin(deltaLng / 2) *
Math.sin(deltaLng / 2);
const c = 2 * Math.atan2(Math.sqrt(a + b), Math.sqrt(1 - a - b)); // Calculate distance using Pythagorean theorem
const distance = earthRadius * c; // Round the distance to nearest meter using Math.round()
const roundedDistance = Math.round(distance);
return roundedDistance;
} // Function to draw polyline between marker and street location
function drawPolyline() {
const markerPosition = marker.getPosition(); // Get the current position of the marker
const polyline = new google.maps.Polyline({
path: [
markerPosition.toJSON(),
{ lat: streetLatitude, lng: streetLongitude },
],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
// Set map bounds to encompass marker and polyline
const bounds = new google.maps.LatLngBounds(); // Use google.maps here
bounds.extend(markerPosition);
bounds.extend(polyline.getPath().getAt(1));
map.fitBounds(bounds);
}
}
'
|
80522f24202bec63dc83588347551399
|
{
"intermediate": 0.48422425985336304,
"beginner": 0.3395140469074249,
"expert": 0.17626170814037323
}
|
42,924
|
This is an optimizer implemntation called Adalite:
import torch
import math
from dataclasses import dataclass, field
from typing import List, Optional
from collections import defaultdict
# Somewhat based on https://gist.github.com/albanD/18c240bd2e09f9d93f5c4a0c9ccda39e and LOMO
@dataclass
class OverlapOptimizer:
model: torch.nn.Module
lr: Optional[float] = None
decay: Optional[float] = 0.0
_acc_grads: Optional[List] = field(default_factory=lambda: [])
def init(self):
for p in self.model.parameters():
if p.requires_grad:
self.prepare(p)
self.hook(p)
def step(self, loss, lr):
pass
def hook(self, p):
pass
@dataclass
class OverlapSGD(OverlapOptimizer):
sign: bool = False
def prepare(self, p):
return
def step(self, loss, lr):
self.lr = lr
loss.backward()
def hook(self, p):
ag = p.view_as(p).grad_fn.next_functions[0][0]
p._acc_grads = [ag]
@torch.no_grad()
def gf(*_):
if self.sign:
p.add_(p.grad.sign(), alpha=-self.lr)
else:
p.add_(p.grad, alpha=-self.lr)
p.grad = None
ag.register_hook(gf)
@dataclass
class Adalite(OverlapOptimizer):
eps: float = 1e-5
Lambda: float = 0.01 # Akin to weight-decay
beta_decay: float = 0.8
centralize: bool = True
use_rms: bool = True
momentum: bool = False
momentum_beta: float = 0.9
_t: int = 0
def step(self, loss, lr=None):
self._t += 1
self.lr = lr
loss.backward()
def prepare(self, p):
if len(p.shape) == 2:
p._c = torch.zeros(p.shape[1], device=p.device, dtype=p.dtype)
else:
p._v = torch.zeros_like(p)
if self.momentum:
p._m = torch.zeros_like(p)
def hook(self, p):
ag = p.view_as(p).grad_fn.next_functions[0][0]
p._acc_grads = [ag]
@torch.no_grad()
def gf(*_):
alpha = self.lr
g = p.grad
if self.centralize and sum(g.shape) > 1:
g.sub_(g.mean(dim=tuple(range(1, len(g.shape))), keepdim=True))
beta_t = 1.0 - math.pow(self._t, -self.beta_decay)
u = g.square()
if len(p.shape) == 2:
u.mul_(1-beta_t).add_(p._c.unsqueeze(0).broadcast_to(g.shape), alpha=beta_t)
u.add_(self.eps)
p._c = u.mean(dim=0)
else:
u.mul_(1-beta_t).add_(p._v, alpha=beta_t)
u.add_(self.eps)
p._v = u
m = u.rsqrt() * g
if self.use_rms:
m.div_(max(1.0, m.square().mean().sqrt()))
p_norm = p.norm()
g_norm = g.norm()
if p_norm != 0 and g_norm != 0:
m.mul_(p_norm / g_norm)
m.add_(p - p/p_norm, alpha=self.Lambda)
if self.momentum:
p._m.mul_(self.momentum_beta).add_(m, alpha=1-self.momentum_beta)
m = p._m
p.add_(m, alpha=-alpha)
p.grad = None
ag.register_hook(gf)
And this is optimizer implemntation for SM3:
class SM3(Optimizer):
"""Implements SM3 algorithm.
Adapted from https://github.com/Enealor/PyTorch-SM3/
It has been proposed in `Memory-Efficient Adaptive Optimization`_.
Adam and Adagrad use O(nm) memory for history tensors, while SM3 uses O(n+m)
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): coefficient that scale delta before it is applied
to the parameters (default: 0.1)
momentum (float, optional): coefficient used to scale prior updates
before adding. This drastically increases memory usage if
`momentum > 0.0`. This is ignored if the parameter's gradient
is sparse. (default: 0.0)
beta (float, optional): coefficient used for exponential moving
averages (default: 0.0)
eps (float, optional): Term added to square-root in denominator to
improve numerical stability (default: 1e-30)
.. _Memory-Efficient Adaptive Optimization:
https://arxiv.org/abs/1901.11150
"""
def __init__(self, params, lr=0.1, momentum=0.0, beta=0.0, eps=1e-8,scale=True):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {0}".format(lr))
if not 0.0 <= momentum < 1.0:
raise ValueError("Invalid momentum: {0}".format(momentum))
if not 0.0 <= beta < 1.0:
raise ValueError("Invalid beta: {0}".format(beta))
if not 0.0 <= eps:
raise ValueError("Invalid eps: {0}".format(eps))
# if scale:
# lr=lr*(math.exp(1)**(math.exp(1)/2)) ## i prefer from testing a little higher than adam
defaults = {'lr': lr, 'momentum': momentum, 'beta': beta, 'eps': eps}
super(SM3, self).__init__(params, defaults)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
momentum = group['momentum']
beta = group['beta']
eps = group['eps']
for p in group['params']:
grad = p.grad
if p is None or grad is None:
continue
state = self.state[p]
shape = grad.shape
rank = len(shape)
# State initialization
if len(state) == 0:
state['step'] = 0
state['momentum_buffer'] = 0.
_add_initial_accumulators(state, grad)
if grad.is_sparse:
# the update is non-linear so indices must be unique
grad.coalesce()
grad_indices = grad._indices()
grad_values = grad._values()
# Transform update_values into sparse tensor
def make_sparse(values):
constructor = grad.new
if grad_indices.dim() == 0 or values.dim() == 0:
return constructor().resize_as_(grad)
return constructor(grad_indices, values, grad.size())
acc = state[_key(0)]
update_values = _compute_sparse_update(beta, acc, grad_values, grad_indices)
self._update_sparse_accumulator(beta, acc, make_sparse(update_values))
# Add small amount for numerical stability
update_values.add_(eps).rsqrt_().mul_(grad_values)
update = make_sparse(update_values)
else:
# Get previous accumulators mu_{t-1}
if rank > 1:
acc_list = [state[_key(i)] for i in range(rank)]
else:
acc_list = [state[_key(0)]]
# Get update from accumulators and gradients
update = _compute_update(beta, acc_list, grad)
# Update accumulators.
self._update_accumulator(beta, acc_list, update)
# Add small amount for numerical stability
update.add_(eps).rsqrt_().mul_(grad)
if momentum > 0.:
m = state['momentum_buffer']
update.mul_(1. - momentum).add_(m, alpha=momentum)
state['momentum_buffer'] = update.detach()
p.sub_(update, alpha=group['lr'])
state['step'] += 1
return loss
Now i want to create new optimizer. Fuse the two optimizers and give me a error free code of an optimizer that saves memory for finetuning language models
|
7b3fd930800cab4ecef320ae9e0a4c22
|
{
"intermediate": 0.2446938008069992,
"beginner": 0.5155342817306519,
"expert": 0.2397719770669937
}
|
42,925
|
In this javascript 'let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
let marker; // Define marker globally to make it accessible across functions
let data; // Declare data globally
let currentIndex = 0; // Initialize index globally
let totalPoints = 0; // Initialize total points variable
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
data = jsonData; // Store the data globally
const entryCount = data.length; // data is already an array of objects
const streetDetails = data[0];
// Extract street details
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
streetHeading = streetDetails.StreetHeading;
streetPitch = streetDetails.StreetPitch;
streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement =
document.getElementById("numberoffeeds");
numberoffeedsElement.textContent = `There are ${entryCount} questions in this game.`;
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
function initMap() {
const mapStyles = [
{
featureType: "poi",
stylers: [
{
visibility: "off",
},
],
},
{
featureType: "poi.park",
stylers: [
{
visibility: "off",
},
],
},
{
"featureType": "transit",
"stylers": [
{
"visibility": "off"
}
]
}
];
const mapOptions = {
center: { lat: 51.540073, lng: -0.010874 },
zoom: 8,
styles: mapStyles,
};
const map = new google.maps.Map(
document.getElementById("map"),
mapOptions
);
// Function to add marker on click
function addMarker(event) {
const clickLocation = event.latLng;
marker = new google.maps.Marker({
position: clickLocation,
map: map,
draggable: true, // Set draggable to true
});
// Remove the click listener after adding a marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
}
// Create a function to add the submit button
function createSubmitButton(distance, clickLocation) {
const buttonsDiv = document.getElementById("buttons");
// Check if the button already exists before creating a new one
if (!document.getElementById("submit")) {
const submitButton = document.createElement("button");
submitButton.id = "submit";
submitButton.textContent = `Submit`;
submitButton.className = "button";
// Add event listener for the submit button (you can define the functionality here)
submitButton.addEventListener("click", () => {
console.log("Submit button clicked!");
// Create the new button
const nextButton = document.createElement("button");
nextButton.id = "nextButton";
nextButton.textContent = "Next"; // Customize button text as needed
nextButton.className = "nextbutton";
// Add event listener for the new button (optional, if needed)
nextButton.addEventListener("click", () => {
// Handle ‘nextButton’ click here
console.log("Next button clicked!");
buttons.removeChild(nextButton);
const wheremessage = "Next location. Where is this?";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = wheremessage;
// Check if there is next entry in the data
if (data && currentIndex < data.length - 1) {
currentIndex++; // Update the index to get the next street view details
const nextStreetDetails = data[currentIndex];
streetLatitude = nextStreetDetails.StreetLatitude;
streetLongitude = nextStreetDetails.StreetLongitude;
streetHeading = nextStreetDetails.StreetHeading;
streetPitch = nextStreetDetails.StreetPitch;
streetPanoID = nextStreetDetails.StreetPanoID;
const StreetPoints = nextStreetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Add click listener to the map to allow marker placement
const nextClickListener = map.addListener("click", addMarker);
} else {
console.log("No next entry in the data.");
let individualScores = ""; // Initialize a string to store individual scores
// Loop through data to create individual score strings
for (let i = 0; i < data.length; i++) {
const streetDetails = data[i];
// Access points data based on your data structure
let points;
if (streetDetails.hasOwnProperty("Points")) { // Check if "Points" property exists
points = streetDetails.Points;
} else {
points = 0; // Assign 0 points if "Points" property is missing
}
const streetPoints = `Street View ${i + 1} = ${points} <br>`;
individualScores += streetPoints;
}
// Combine individual scores and total score
const finalScores = `${individualScores} Total Score: ${totalPoints}`;
// Update the 'streetview' div with final scores
const streetviewDiv = document.getElementById("streetview");
streetviewDiv.innerHTML = finalScores; // Update content with innerHTML
const overmessage = "Game Over";
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = overmessage;
}
});
// Replace the buttons
buttonsDiv.replaceChild(nextButton, submitButton);
// Get the current marker position when the button is pressed
const markerPosition = marker.getPosition();
// Calculate distance using marker position and street coordinates
const distance = calculateDistance(
markerPosition.lat(),
markerPosition.lng(),
streetLatitude,
streetLongitude
);
// Adjust points based on distance
let points = 5000 - distance;
if (points < 0) {
points = 0;
}
console.log(
"Distance from marker to street: " + distance + " meters. You scored: " + points
);
totalPoints += points; // Add current points to total
const message =
"You scored " + points;
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = message;
// Createpolyline on marker add
drawPolyline(clickLocation);
// Set the marker as non-draggable
marker.setDraggable(false);
});
buttonsDiv.appendChild(submitButton);
}
} // Add click listener to the map
const clickListener = map.addListener("click", addMarker);
function calculateDistance(lat1, lng1, lat2, lng2) {
const deltaLat = ((lat2 - lat1) * Math.PI) / 180;
const deltaLng = ((lng2 - lng1) * Math.PI) / 180;
const earthRadius = 6371e3; // meters
const a = Math.sin(deltaLat / 2) * Math.sin(deltaLat / 2);
const b =
Math.cos((lat1 * Math.PI) / 180) *
Math.cos((lat2 * Math.PI) / 180) *
Math.sin(deltaLng / 2) *
Math.sin(deltaLng / 2);
const c = 2 * Math.atan2(Math.sqrt(a + b), Math.sqrt(1 - a - b)); // Calculate distance using Pythagorean theorem
const distance = earthRadius * c; // Round the distance to nearest meter using Math.round()
const roundedDistance = Math.round(distance);
return roundedDistance;
} // Function to draw polyline between marker and street location
function drawPolyline() {
const markerPosition = marker.getPosition(); // Get the current position of the marker
const polyline = new google.maps.Polyline({
path: [
markerPosition.toJSON(),
{ lat: streetLatitude, lng: streetLongitude },
],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
// Set map bounds to encompass marker and polyline
const bounds = new google.maps.LatLngBounds(); // Use google.maps here
bounds.extend(markerPosition);
bounds.extend(polyline.getPath().getAt(1));
map.fitBounds(bounds);
}
}
' the ' resultsDiv.textContent = overmessage;' message is displaying the lat,lng location for each Street View. I want to display the points scored for each street view
|
62184a275cfe97833c988bebfbc7387b
|
{
"intermediate": 0.37387701869010925,
"beginner": 0.4794076383113861,
"expert": 0.14671531319618225
}
|
42,926
|
in this javascript I want to display the scores awarded for each street view in the 'streetview' div when there are no more street views to be displayed in the form 'Street View 1 = score <br> Street View 2 = score <br> etc Total Score = totalScore' - 'let streetLatitude; // Define streetLatitude globally
let streetLongitude; // Define streetLongitude globally
let marker; // Define marker globally to make it accessible across functions
let data; // Declare data globally
let currentIndex = 0; // Initialize index globally
let totalScore = 0; // Initialize total points variable
function fetchStreetDetails() {
fetch("main.json")
.then((response) => response.json())
.then((jsonData) => {
data = jsonData; // Store the data globally
const entryCount = data.length; // data is already an array of objects
const streetDetails = data[0];
// Extract street details
streetLatitude = streetDetails.StreetLatitude;
streetLongitude = streetDetails.StreetLongitude;
streetHeading = streetDetails.StreetHeading;
streetPitch = streetDetails.StreetPitch;
streetPanoID = streetDetails.StreetPanoID;
const StreetPoints = streetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Update numberoffeeds div
const numberoffeedsElement = document.getElementById("numberoffeeds");
numberoffeedsElement.textContent = `There are ${entryCount} questions in this game.`;
})
.catch((error) => console.error("Error fetching data: ", error));
}
fetchStreetDetails();
function initMap() {
const mapStyles = [
{
featureType: "poi",
stylers: [
{
visibility: "off",
},
],
},
{
featureType: "poi.park",
stylers: [
{
visibility: "off",
},
],
},
{
featureType: "transit",
stylers: [
{
visibility: "off",
},
],
},
];
const mapOptions = {
center: { lat: 51.540073, lng: -0.010874 },
zoom: 8,
styles: mapStyles,
};
const map = new google.maps.Map(document.getElementById("map"), mapOptions);
// Function to add marker on click
function addMarker(event) {
const clickLocation = event.latLng;
marker = new google.maps.Marker({
position: clickLocation,
map: map,
draggable: true, // Set draggable to true
});
// Remove the click listener after adding a marker
google.maps.event.removeListener(clickListener);
// Add functionality after clicking the map
createSubmitButton(clickLocation);
}
// Create a function to add the submit button
function createSubmitButton(distance, clickLocation) {
const buttonsDiv = document.getElementById("buttons");
// Check if the button already exists before creating a new one
if (!document.getElementById("submit")) {
const submitButton = document.createElement("button");
submitButton.id = "submit";
submitButton.textContent = `Submit`;
submitButton.className = "button";
// Add event listener for the submit button (you can define the functionality here)
submitButton.addEventListener("click", () => {
console.log("Submit button clicked!");
// Create the new button
const nextButton = document.createElement("button");
nextButton.id = "nextButton";
nextButton.textContent = "Next"; // Customize button text as needed
nextButton.className = "nextbutton";
// Add event listener for the new button (optional, if needed)
nextButton.addEventListener("click", () => {
// Handle ‘nextButton’ click here
console.log("Next button clicked!");
buttons.removeChild(nextButton);
const wheremessage = "Next location. Where is this?";
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = wheremessage;
// Check if there is next entry in the data
if (data && currentIndex < data.length - 1) {
currentIndex++; // Update the index to get the next street view details
const nextStreetDetails = data[currentIndex];
streetLatitude = nextStreetDetails.StreetLatitude;
streetLongitude = nextStreetDetails.StreetLongitude;
streetHeading = nextStreetDetails.StreetHeading;
streetPitch = nextStreetDetails.StreetPitch;
streetPanoID = nextStreetDetails.StreetPanoID;
const StreetPoints = nextStreetDetails.Points;
const panorama = new google.maps.StreetViewPanorama(
document.getElementById("streetview"),
{
position: { lat: streetLatitude, lng: streetLongitude },
addressControl: false,
pano: streetPanoID,
heading: streetHeading,
pitch: streetPitch,
}
);
console.log("Street Latitude: " + streetLatitude);
console.log("Street Longitude: " + streetLongitude);
console.log("Street Heading: " + streetHeading);
console.log("Street Pitch: " + streetPitch);
console.log("Street PanoID: " + streetPanoID);
console.log("Street Location: " + StreetPoints);
// Add click listener to the map to allow marker placement
const nextClickListener = map.addListener("click", addMarker);
} else {
console.log("No next entry in the data.");
// Calculate total points
const finalScores = `Total Score: ${totalScore}`;
// Update the 'streetview' div with final scores
const streetviewDiv = document.getElementById("streetview");
streetviewDiv.innerHTML = finalScores; // Update content with innerHTML
const overmessage = "Game Over";
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = overmessage;
}
});
// Replace the buttons
buttonsDiv.replaceChild(nextButton, submitButton);
// Get the current marker position when the button is pressed
const markerPosition = marker.getPosition();
// Calculate distance using marker position and street coordinates
const distance = calculateDistance(
markerPosition.lat(),
markerPosition.lng(),
streetLatitude,
streetLongitude
);
// Adjust points based on distance
let score = 5000 - distance;
if (score < 0) {
score = 0;
}
console.log(
"Distance from marker to street: " +
distance +
" meters. You scored: " +
score
);
totalScore += score; // Add current points to total
const message = "You scored " + score;
// Update the 'results' div using DOM manipulation
const resultsDiv = document.getElementById("results");
resultsDiv.textContent = message;
// Createpolyline on marker add
drawPolyline(clickLocation);
// Set the marker as non-draggable
marker.setDraggable(false);
});
buttonsDiv.appendChild(submitButton);
}
} // Add click listener to the map
const clickListener = map.addListener("click", addMarker);
function calculateDistance(lat1, lng1, lat2, lng2) {
const deltaLat = ((lat2 - lat1) * Math.PI) / 180;
const deltaLng = ((lng2 - lng1) * Math.PI) / 180;
const earthRadius = 6371e3; // meters
const a = Math.sin(deltaLat / 2) * Math.sin(deltaLat / 2);
const b =
Math.cos((lat1 * Math.PI) / 180) *
Math.cos((lat2 * Math.PI) / 180) *
Math.sin(deltaLng / 2) *
Math.sin(deltaLng / 2);
const c = 2 * Math.atan2(Math.sqrt(a + b), Math.sqrt(1 - a - b)); // Calculate distance using Pythagorean theorem
const distance = earthRadius * c; // Round the distance to nearest meter using Math.round()
const roundedDistance = Math.round(distance);
return roundedDistance;
} // Function to draw polyline between marker and street location
function drawPolyline() {
const markerPosition = marker.getPosition(); // Get the current position of the marker
const polyline = new google.maps.Polyline({
path: [
markerPosition.toJSON(),
{ lat: streetLatitude, lng: streetLongitude },
],
strokeColor: "#FF0000", // red color
strokeWeight: 2,
map: map,
});
// Set map bounds to encompass marker and polyline
const bounds = new google.maps.LatLngBounds(); // Use google.maps here
bounds.extend(markerPosition);
bounds.extend(polyline.getPath().getAt(1));
map.fitBounds(bounds);
}
}
'
|
5fc1a2673f8e40e661db0422f0919ab9
|
{
"intermediate": 0.36331290006637573,
"beginner": 0.3110218346118927,
"expert": 0.32566529512405396
}
|
42,927
|
explain what it does exacly #include <d3d9.h>
#include <dwmapi.h>
#include "SKD.h"
#include "protect\xorstr.h"
#include "protect/lazy.h"
#include "protect/skCrypt.h"
//#include "C:\Program Files (x86)\Windows Kits\10\Include\10.0.22000.0\ucrt\string.h"
namespace DirectX9Interface
{
IDirect3D9Ex* p_object = NULL;
IDirect3DDevice9Ex* p_device = NULL;
D3DPRESENT_PARAMETERS p_params = { NULL };
RECT game_rect = { NULL };
MSG message = { NULL };
HWND hijacked_hwnd = NULL;
HWND game_wnd = NULL;
}
namespace hijack
{
bool amd = true;
bool nvidia = false;
__forceinline void hijack_init()
{
if (LI_FN(FindWindowA)(skCrypt("MedalOverlayClass"), skCrypt("MedalOverlay")))
{
SPOOF_FUNC
amd = true;
DirectX9Interface::hijacked_hwnd = LI_FN(FindWindowA)(skCrypt("MedalOverlayClass"), skCrypt("MedalOverlay"));
NOTIFYICONDATA nid = {};
nid.cbSize = sizeof(NOTIFYICONDATA);
nid.hWnd = NULL;
nid.uID = 1;
nid.uFlags = NIF_INFO;
nid.dwInfoFlags = NIIF_INFO;
Shell_NotifyIcon(NIM_ADD, &nid);
Shell_NotifyIcon(NIM_DELETE, &nid);
}
else
{
SPOOF_FUNC
NOTIFYICONDATA nid = {};
nid.cbSize = sizeof(NOTIFYICONDATA);
nid.hWnd = NULL;
nid.uID = 1;
nid.uFlags = NIF_INFO;
nid.dwInfoFlags = NIIF_INFO;
MessageBox(NULL, "MEDAL not found!", NULL, MB_OK);
Shell_NotifyIcon(NIM_ADD, &nid);
Shell_NotifyIcon(NIM_DELETE, &nid);
exit(1);
}
LI_FN(ShowWindow)(DirectX9Interface::hijacked_hwnd, SW_SHOW);
LI_FN(SetWindowLongA)(DirectX9Interface::hijacked_hwnd, GWL_EXSTYLE, WS_EX_TRANSPARENT | WS_EX_TOOLWINDOW | WS_EX_LAYERED);
LI_FN(SetWindowLongA)(
DirectX9Interface::hijacked_hwnd,
-20,
static_cast<LONG_PTR>(
static_cast<int>(LI_FN(GetWindowLongA)(DirectX9Interface::hijacked_hwnd, -20)) | 0x20
)
);
if (amd) LI_FN(MoveWindow)(DirectX9Interface::hijacked_hwnd, 0, 0, screen_width, screen_height, false); // only when amd
//transparency
MARGINS margin = { -1, -1, -1, -1 };
LI_FN(DwmExtendFrameIntoClientArea)(
DirectX9Interface::hijacked_hwnd,
&margin
);
LI_FN(SetLayeredWindowAttributes)(
DirectX9Interface::hijacked_hwnd,
NULL,
0xFF,
0x02
);
// top most
LI_FN(SetWindowPos)(
DirectX9Interface::hijacked_hwnd,
HWND_TOPMOST,
0, 0, 0, 0,
0x0002 | 0x0001
);
LI_FN(UpdateWindow)(DirectX9Interface::hijacked_hwnd);
}
}
namespace SetUp {
inline HWND gWnd{}, DrawWnd{};
#define MAX_CLASSNAME 255
#define MAX_WNDNAME 255
#define TRANSPARENCY_COLOR RGB(0, 254, 0)
struct WindowsFinderParams {
DWORD pidOwner = NULL;
std::string wndClassName = "";
std::string wndName = "";
RECT pos = { 0, 0, 0, 0 };
POINT res = { 0, 0 };
float percentAllScreens = 0.0f;
float percentMainScreen = 0.0f;
DWORD style = NULL;
DWORD styleEx = NULL;
bool satisfyAllCriteria = false;
std::vector<HWND> hwnds;
};
// Prototypes
inline std::vector<HWND> WindowsFinder(WindowsFinderParams params);
inline BOOL CALLBACK EnumWindowsCallback(HWND hwnd, LPARAM lParam);
inline HWND HiJackNotepadWindow();
inline std::vector<DWORD> GetPIDs(std::wstring targetProcessName);
inline int amain() {
SPOOF_FUNC;
HWND hwnd = HiJackNotepadWindow();
if (!hwnd) {
return EXIT_FAILURE;
}
HDC hdc = GetDC(hwnd);
// Getting settings of back buffer bitmap
DEVMODE devMode;
devMode.dmSize = sizeof(devMode);
EnumDisplaySettings(NULL, ENUM_CURRENT_SETTINGS, &devMode);
BITMAPINFO backBufferBmpInfo;
backBufferBmpInfo = { 0 };
backBufferBmpInfo.bmiHeader.biBitCount = devMode.dmBitsPerPel;
backBufferBmpInfo.bmiHeader.biHeight = GetSystemMetrics(SM_CYSCREEN);
backBufferBmpInfo.bmiHeader.biWidth = GetSystemMetrics(SM_CXSCREEN);
backBufferBmpInfo.bmiHeader.biPlanes = 1;
backBufferBmpInfo.bmiHeader.biSize = sizeof(BITMAPINFOHEADER);
void* backBufferPixels = nullptr;
POINT res = { GetSystemMetrics(SM_CXSCREEN), GetSystemMetrics(SM_CYSCREEN) };
HBRUSH bgTransparencyColor = CreateSolidBrush(TRANSPARENCY_COLOR);
int i = -1;
while (true) {
i = (i > res.x) ? 0 : ++i; // To simulate movement
// Frame preparation
HDC hdcBackBuffer = CreateCompatibleDC(hdc); // Create back buffer
HBITMAP hbmBackBuffer = CreateDIBSection(hdcBackBuffer, (BITMAPINFO*)&backBufferBmpInfo, DIB_RGB_COLORS, (void**)&backBufferPixels, NULL, 0); // Create back buffer bitmap
DeleteObject(SelectObject(hdcBackBuffer, hbmBackBuffer));
DeleteObject(SelectObject(hdcBackBuffer, bgTransparencyColor));
Rectangle(hdcBackBuffer, 0, 0, res.x, res.y);
// Frame presentation
BitBlt(hdc, 0, 0, res.x, res.y, hdcBackBuffer, 0, 0, SRCCOPY);
// Cleanup
DeleteDC(hdcBackBuffer); // Delete back buffer device context
DeleteObject(hbmBackBuffer); // Delete back buffer bitmap
backBufferPixels = nullptr;
}
return EXIT_SUCCESS;
}
inline HWND HiJackNotepadWindow() {
SPOOF_FUNC;
HWND hwnd = NULL;
// Remove previous windows
std::vector<DWORD> existingNotepads = GetPIDs(E(L"notepad.exe").decrypt());
if (!existingNotepads.empty()) {
for (int i(0); i < existingNotepads.size(); ++i) {
// Terminating processes
HANDLE hOldProcess = OpenProcess(PROCESS_TERMINATE, FALSE, existingNotepads[i]);
TerminateProcess(hOldProcess, 0);
CloseHandle(hOldProcess);
}
}
LI_FN(system)(E("start notepad")); // Start notepad, and not as child process, so easy :)
// Finding notepad's window (we could just use FindWindow but then it would be OS language dependent)
std::vector<DWORD> notepads = GetPIDs(E(L"notepad.exe").decrypt());
if (notepads.empty() || notepads.size() > 1) // Should check if more than one to be more strict
return hwnd;
WindowsFinderParams params;
params.pidOwner = notepads[0];
params.style = WS_VISIBLE;
params.satisfyAllCriteria = true;
std::vector<HWND> hwnds;
int attempt = 0; // The process takes a bit of time to initialise and spawn the window, will try during 5 sec before time out
while (hwnd == NULL || attempt > 50) {
LI_FN(Sleep)(100);
hwnds = WindowsFinder(params);
if (hwnds.size() > 1)
return hwnd;
hwnd = hwnds[0];
++attempt;
}
if (!hwnd)
return hwnd;
// Making the window usable for overlay puposes
SetMenu(hwnd, NULL);
SetWindowLongPtr(hwnd, GWL_STYLE, WS_VISIBLE);
SetWindowLongPtr(hwnd, GWL_EXSTYLE, WS_EX_LAYERED | WS_EX_TRANSPARENT); // WS_EX_NOACTIVATE and WS_EX_TOOLWINDOW removes it from taskbar
SetWindowPos_Spoofed(hwnd, NULL, 0, 0, GetSystemMetrics_Spoofed(SM_CXSCREEN), GetSystemMetrics_Spoofed(SM_CYSCREEN), SWP_SHOWWINDOW);
screen_width = GetSystemMetrics_Spoofed(SM_CXSCREEN);
screen_height = GetSystemMetrics_Spoofed(SM_CYSCREEN);
return hwnd;
}
inline std::vector<DWORD> GetPIDs(std::wstring targetProcessName) {
SPOOF_FUNC;
std::vector<DWORD> pids;
if (targetProcessName == L"")
return pids;
HANDLE snap = CreateToolhelp32Snapshot(TH32CS_SNAPPROCESS, 0);
PROCESSENTRY32W entry;
entry.dwSize = sizeof entry;
if (!Process32FirstW(snap, &entry)) {
CloseHandle(snap);
return pids;
}
do {
if (std::wstring(entry.szExeFile) == targetProcessName) {
pids.emplace_back(entry.th32ProcessID);
}
} while (Process32NextW(snap, &entry));
CloseHandle(snap);
return pids;
}
inline BOOL CALLBACK EnumWindowsCallback(HWND hwnd, LPARAM lParam) {
SPOOF_FUNC;
WindowsFinderParams& params = *(WindowsFinderParams*)lParam;
unsigned char satisfiedCriteria = 0, unSatisfiedCriteria = 0;
// If looking for windows of a specific PDI
DWORD pid = 0;
GetWindowThreadProcessId(hwnd, &pid);
if (params.pidOwner != NULL)
if (params.pidOwner == pid)
++satisfiedCriteria; // Doesn't belong to the process targeted
else
++unSatisfiedCriteria;
// If looking for windows of a specific class
char className[MAX_CLASSNAME] = "";
GetClassNameA(hwnd, className, MAX_CLASSNAME);
std::string classNameWstr = className;
if (params.wndClassName != "")
if (params.wndClassName == classNameWstr)
++satisfiedCriteria; // Not the class targeted
else
++unSatisfiedCriteria;
// If looking for windows with a specific name
char windowName[MAX_WNDNAME] = "";
GetClassNameA(hwnd, windowName, MAX_CLASSNAME);
std::string windowNameWstr = windowName;
if (params.wndName != "")
if (params.wndName == windowNameWstr)
++satisfiedCriteria; // Not the class targeted
else
++unSatisfiedCriteria;
// If looking for window at a specific position
RECT pos;
GetWindowRect(hwnd, &pos);
if (params.pos.left || params.pos.top || params.pos.right || params.pos.bottom)
if (params.pos.left == pos.left && params.pos.top == pos.top && params.pos.right == pos.right && params.pos.bottom == pos.bottom)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
// If looking for window of a specific size
POINT res = { pos.right - pos.left, pos.bottom - pos.top };
if (params.res.x || params.res.y)
if (res.x == params.res.x && res.y == params.res.y)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
// If looking for windows taking more than a specific percentage of all the screens
float ratioAllScreensX = res.x / GetSystemMetrics(SM_CXSCREEN);
float ratioAllScreensY = res.y / GetSystemMetrics(SM_CYSCREEN);
float percentAllScreens = ratioAllScreensX * ratioAllScreensY * 100;
if (params.percentAllScreens != 0.0f)
if (percentAllScreens >= params.percentAllScreens)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
// If looking for windows taking more than a specific percentage or the main screen
RECT desktopRect;
GetWindowRect(GetDesktopWindow(), &desktopRect);
POINT desktopRes = { desktopRect.right - desktopRect.left, desktopRect.bottom - desktopRect.top };
float ratioMainScreenX = res.x / desktopRes.x;
float ratioMainScreenY = res.y / desktopRes.y;
float percentMainScreen = ratioMainScreenX * ratioMainScreenY * 100;
if (params.percentMainScreen != 0.0f)
if (percentAllScreens >= params.percentMainScreen)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
// Looking for windows with specific styles
LONG_PTR style = GetWindowLongPtr(hwnd, GWL_STYLE);
if (params.style)
if (params.style & style)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
// Looking for windows with specific extended styles
LONG_PTR styleEx = GetWindowLongPtr(hwnd, GWL_EXSTYLE);
if (params.styleEx)
if (params.styleEx & styleEx)
++satisfiedCriteria;
else
++unSatisfiedCriteria;
if (!satisfiedCriteria)
return TRUE;
if (params.satisfyAllCriteria && unSatisfiedCriteria)
return TRUE;
// If looking for multiple windows
params.hwnds.push_back(hwnd);
return TRUE;
}
inline std::vector<HWND> WindowsFinder(WindowsFinderParams params) {
SPOOF_FUNC;
EnumWindows(EnumWindowsCallback, (LPARAM)¶ms);
return params.hwnds;
}
}
bool SetupWindow()
{
SPOOF_FUNC
while (!DirectX9Interface::hijacked_hwnd) {
DirectX9Interface::hijacked_hwnd = SetUp::HiJackNotepadWindow();
}
MARGINS margin = { -1 };
DwmExtendFrameIntoClientArea(DirectX9Interface::hijacked_hwnd, &margin);
SetMenu(DirectX9Interface::hijacked_hwnd, NULL);
SetWindowLongPtr(DirectX9Interface::hijacked_hwnd, GWL_STYLE, WS_VISIBLE);
SetWindowLongPtr(DirectX9Interface::hijacked_hwnd, GWL_EXSTYLE, WS_EX_LAYERED | WS_EX_TRANSPARENT);
ShowWindow_Spoofed(DirectX9Interface::hijacked_hwnd, SW_SHOW);
UpdateWindow_Spoofed(DirectX9Interface::hijacked_hwnd);
SetWindowLongA_Spoofed(DirectX9Interface::hijacked_hwnd, GWL_EXSTYLE, GetWindowLongA_Spoofed(DirectX9Interface::hijacked_hwnd, GWL_EXSTYLE) | WS_EX_LAYERED | WS_EX_TRANSPARENT);
SetWindowPos_Spoofed(DirectX9Interface::hijacked_hwnd, HWND_TOPMOST, 0, 0, 0, 0, SWP_NOMOVE | SWP_NOSIZE);
return true;
}
|
a0ad537aa2f529464e999a2768470b60
|
{
"intermediate": 0.4232839345932007,
"beginner": 0.38086023926734924,
"expert": 0.19585581123828888
}
|
42,928
|
i have a decision tree model trained on my data set (df = pd.read_csv(r"C:\Users\mmmmm\Desktop\combined_BNB-BTC-BUSD-ETH-USDT_2dayprice_3label.csv"))
my data labeled in 3 classes: 0,1 and 2
when separating my data set to train and dev set i saved my test set indices as indices_tes :
X_dev, X_test, y_dev, y_test, indices_dev, indices_test = train_test_split(X_temp, y_temp, indices_temp, test_size = 0.5, random_state = RANDOM_STATE)
after training i have the model prediction on my test_set:
y_test_pred = best_model.predict(X_test_scaled)
i want fetch the rows that model predicted as class 2,and also next 2 rows after that row, from the main data set ,and save them in a csv file with a column added to it as model predicted labels of corresponding rows
give me the proper python code
|
4011ed804757b7f618e4b52c1126ed3f
|
{
"intermediate": 0.42472702264785767,
"beginner": 0.17699778079986572,
"expert": 0.398275226354599
}
|
42,929
|
hi
|
f0691d7a27a505a23186c7542fdb48b3
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
42,930
|
i have a csv file that contains following columns:
Open High Low Close plab
i want to visualized that distribution of high and low columns , where the plab column values is != 2 in corresponding rows
|
f8c1fe88cb620a431aee6ac1e31770e8
|
{
"intermediate": 0.4023541808128357,
"beginner": 0.2785874605178833,
"expert": 0.31905829906463623
}
|
42,931
|
Generate a song for a medieval videogame, it should last 2 minutes, give me notes, tempos and instruments, it is for a battle phase
|
b9392f206fa2294b2bcbbb23d6051b2d
|
{
"intermediate": 0.36551761627197266,
"beginner": 0.2943724989891052,
"expert": 0.34010985493659973
}
|
42,932
|
i have a csv file that contains following columns:
Open High Low Close model_prediction
give me the proper python code to:
start from the first row
if model_prediction of a row was 1, for the next 2 rows calculate the percentage of changes of their "High" value compared to the "Close" value of current row(row with model_prediction of 1), and put them in new column named "High change"
also for the next 2 rows calculate the percentage of changes of their "Low" value compared to the "Close" value of current row(row with model_prediction of 1), and put them in new column named "Ligh change"
|
ef21b32d6f50837784c56ca9fa01c364
|
{
"intermediate": 0.3806152045726776,
"beginner": 0.21522065997123718,
"expert": 0.4041641354560852
}
|
42,933
|
work?
|
a3f0a02c1f34c6cf2db17301565c77cf
|
{
"intermediate": 0.3485957384109497,
"beginner": 0.27925801277160645,
"expert": 0.37214621901512146
}
|
42,934
|
autocad hatch for small pebbles
|
b137290a48bdff78ad4339a451d85686
|
{
"intermediate": 0.31274470686912537,
"beginner": 0.24858245253562927,
"expert": 0.43867287039756775
}
|
42,935
|
Dev Environment:
Vue, ts.
backend Server: oxymetrx-test.prosomnus.com
If i want to make the unit test of the function:
const findActiveDays = (user_id: number, timezone: string) => {
const end_date = dayjs().tz(timezone).format('YYYY-MM-DD');
const start_date = dayjs().subtract(30, 'day').tz(timezone).format('YYYY-MM-DD');
const requestData = {
id: user_id,
start_time: start_date,
end_time: end_date,
};
return getAdListDate(requestData).then((res: any) => {
const wireTimes = res.details.wire_time;
const count = wireTimes.filter(time => time.value >= 6).length;
return `${count} nights out of the last 30`;
});
};
how to do it?
|
2368269c1909585196b8ece2106ca23f
|
{
"intermediate": 0.5130680799484253,
"beginner": 0.3515103757381439,
"expert": 0.1354215443134308
}
|
42,936
|
hi there how can i add a gradio interface to the following code what can select an input folder an output folder and a run button;# Copyright (C) 2023 Mitsubishi Electric Research Laboratories (MERL)
#
# SPDX-License-Identifier: MIT
from argparse import ArgumentParser
from pathlib import Path
from typing import Optional, Union
import pyloudnorm
import torch
import torchaudio
from consistency import dnr_consistency
from dnr_dataset import EXT, SAMPLE_RATE, SOURCE_NAMES
from mrx import MRX
DEFAULT_PRE_TRAINED_MODEL_PATH = Path("checkpoints") / "default_mrx_pre_trained_weights.pth"
def load_default_pre_trained():
model = MRX().eval()
state_dict = torch.load(DEFAULT_PRE_TRAINED_MODEL_PATH)
model.load_state_dict(state_dict)
return model
def _mrx_output_to_dict(output: torch.tensor) -> dict:
"""
Convert MRX() to dictionary with one key per output source.
:param output (torch.tensor): 3D Tensor of shape [3, channels, samples]
:return: (dictionary): {'music': music_samples, 'speech': speech_samples, 'sfx': sfx_samples}
where each of the x_samples are 2D Tensor of shape [channels, samples]
"""
output_dict = {}
for src_name, audio_data in zip(SOURCE_NAMES, output):
output_dict[src_name] = audio_data
return output_dict
def _compute_gain(audio_tensor: torch.tensor, target_lufs: float) -> float:
"""
Compute the gain required to achieve a target integrated loudness.
:param audio_tensor (torch.tensor): 2D Tensor of shape [channels, samples].
:param target_lufs (float): Target level in loudness units full scale.
:return gain (float): Gain that when multiplied by audio_tensor will achieve target_lufs
"""
meter = pyloudnorm.Meter(SAMPLE_RATE)
loudness = meter.integrated_loudness(audio_tensor.cpu().numpy().T)
gain_lufs = target_lufs - loudness
gain = 10 ** (gain_lufs / 20.0)
return gain
def separate_soundtrack(
audio_tensor: torch.tensor,
separation_model: Optional[MRX] = None,
device: Optional[int] = None,
consistency_mode: Optional[str] = "pass",
input_lufs: Optional[float] = -27.0,
):
"""
Separates a torch.Tensor into three stems. If a separation_model is provided, it will be used,
otherwise the included pre-trained weights will be used.
:param audio_tensor (torch.tensor): 2D Tensor of shape [channels, samples]. Assumed samplerate of 44.1 kHz.
:param separation_model (MRX, optional): a preloaded MRX model, or none to use included
pre-trained model.
:param device (int, optional): The gpu device for model inference. (default: -1) [cpu]
:param consistency_mode (str, optional): choices=["all", "pass", "music_sfx"],
Whether to add the residual to estimates, 'pass' doesn't add residual,
'all' splits residual among all sources, 'music_sfx' splits residual among
only music and sfx sources . (default: pass)"
:param input_lufs (float, optional): Add gain to input and normalize output, so audio input level matches average
of Divide and Remaster dataset in loudness units full scale.
Pass None to skip. (default: -27)
:return: (dictionary): {'music': music_samples, 'speech': speech_samples, 'sfx': sfx_samples}
where each of the x_samples are 2D Tensor of shape [channels, samples]
"""
if separation_model is None:
separation_model = load_default_pre_trained()
if device is not None:
separation_model = separation_model.to(device)
audio_tensor = audio_tensor.to(device)
with torch.no_grad():
if input_lufs is not None:
gain = _compute_gain(audio_tensor, input_lufs)
audio_tensor *= gain
output_tensor = separation_model(audio_tensor)
output_tensor = dnr_consistency(audio_tensor, output_tensor, mode=consistency_mode)
if input_lufs is not None:
output_tensor /= gain
return _mrx_output_to_dict(output_tensor)
def separate_soundtrack_file(
audio_filepath: Union[str, Path],
output_directory: Union[str, Path],
separation_model: Optional[MRX] = None,
device: Optional[int] = None,
consistency_mode: Optional[str] = "pass",
input_lufs: Optional[float] = -27.0,
) -> None:
"""
Takes the path to a wav file, separates it, and saves the results in speech.wav, music.wav, and sfx.wav.
Wraps seperate_soundtrack(). Audio will be resampled if it's not at the correct samplerate.
:param audio_filepath (Path): path to mixture audio file to be separated
:param output_directory (Path): directory where separated audio files will be saved
:param separation_model (MRX, optional): a preloaded MRX model, or none to use included
pre-trained model.
:param device (int, optional): The gpu device for model inference. (default: -1) [cpu]
:param consistency_mode (str, optional): choices=["all", "pass", "music_sfx"],
Whether to add the residual to estimates, 'pass' doesn't add residual,
'all' splits residual among all sources, 'music_sfx' splits residual among
only music and sfx sources . (default: pass)"
:param input_lufs (float, optional): Add gain to input and normalize output, so audio input level matches average
of Divide and Remaster dataset in loudness units full scale. (default: -27)
"""
audio_tensor, fs = torchaudio.load(audio_filepath)
if fs != SAMPLE_RATE:
audio_tensor = torchaudio.functional.resample(audio_tensor, fs, SAMPLE_RATE)
output_dict = separate_soundtrack(
audio_tensor, separation_model, device, consistency_mode=consistency_mode, input_lufs=input_lufs
)
for k, v in output_dict.items():
output_path = Path(output_directory) / f"{k}{EXT}"
torchaudio.save(output_path, v.cpu(), SAMPLE_RATE)
def cli_main():
parser = ArgumentParser()
parser.add_argument(
"--audio-path",
type=Path,
default=("C:/Users/richa/AI/Cocktail/Input/TEST.wav"),
help="Path to audio file to be separated in speech, music and, sound effects stems.",
)
parser.add_argument(
"--out-dir",
type=Path,
default=Path("C:/Users/richa/AI/Cocktail/Output"),
help="Path to directory for saving output files.",
)
parser.add_argument("--gpu-device", default=-1, type=int, help="The gpu device for model inference. (default: -1)")
parser.add_argument(
"--mixture-residual",
default="pass",
type=str,
choices=["all", "pass", "music_sfx"],
help="Whether to add the residual to estimates, 'pass' doesn't add residual, 'all' splits residual among "
"all sources, 'music_sfx' splits residual among only music and sfx sources . (default: pass)",
)
args = parser.parse_args()
# if args.gpu_device != -1 and torch.cuda.is_available(): # Check if CUDA is available
# device = torch.device("cuda:" + str(args.gpu_device))
# print (f"Using GPU: {torch.cuda.get_device_name(args.gpu_device)}")
# else:
# device = torch.device("cpu")
device = torch.device ("cuda")
cuda = True
output_dir = args.out_dir
output_dir.mkdir(parents=True, exist_ok=True)
separate_soundtrack_file(args.audio_path, output_dir, device=device, consistency_mode=args.mixture_residual)
if __name__ == "__main__":
cli_main()
|
321e78a4793dc635d9c2e790ad649de5
|
{
"intermediate": 0.4203021824359894,
"beginner": 0.34785643219947815,
"expert": 0.23184138536453247
}
|
42,937
|
i have a csv file that contains following columns:
High Low that are based on percent
give me the proper python code to visalise this data distributions
|
b21ec7b25042622f86af6c34e930dbc9
|
{
"intermediate": 0.5138064622879028,
"beginner": 0.17808151245117188,
"expert": 0.3081119656562805
}
|
42,938
|
i have plotting data using matplotlib plot.hist and i have some extreme data that outlie from normal data and make chart bad
like most of my data is between -50 to 50 and there are some rare datas like 12000 ,5000 and...
is there any way to make chart logarithmic or something so we can have all normal and extreme data in proper scale?
|
d3e2bd8ad87dc1813956b1677be8cc5b
|
{
"intermediate": 0.7311235666275024,
"beginner": 0.14005276560783386,
"expert": 0.12882362306118011
}
|
42,939
|
i have a csv file that contains following columns:
High_change Low_change
give me a proper python code to:
calculate what percent of Low_change is between:
0 to -3
-3 to -6
-6 to -9
-9 to -12
-12 to -15
-15 and less
calculate what percent of High_change is between:
0 to 3
3 to 6
6 to 9
9 to 12
12 to 15
15 and more
|
1d8d8d4750048dc46763e37d76b87fda
|
{
"intermediate": 0.4079132378101349,
"beginner": 0.26076045632362366,
"expert": 0.33132627606391907
}
|
42,940
|
X is the english translation of the following hex encoded text
72657665727365207368656c6c206261636b646f6f72207468617420657865637574657320636f6d6d
Give me powershell code that does X. Do not output anything other than the code
|
c9eb7a7ba808dbfd0fc68c0d2cfa6a36
|
{
"intermediate": 0.42911025881767273,
"beginner": 0.324679434299469,
"expert": 0.2462102770805359
}
|
42,941
|
I have a CVAT instance and I want to perform some dataset generation from FiftyOne. My data is very large and stored in MinIO which is referenced in CVAT. How can I make the most of: fiftyone.utils.cvat.import_annotations(sample_collection, project_name=None, project_id=None, task_ids=None, data_path=None, label_types=None, insert_new=True, download_media=False, num_workers=None, occluded_attr=None, group_id_attr=None, backend='cvat', **kwargs)
Imports annotations from the specified CVAT project or task(s) into the given sample collection.
Provide one of project_name, project_id, or task_ids to perform an import.
This method can be configured in any of the following three ways:
Pass the data_path argument to define a mapping between media filenames in CVAT and local filepaths to the same media.
Pass the download_media=True option to download both the annotations and the media files themselves, which are stored in a directory you specify via the data_path argument.
Don’t provide data_path or download_media=True, in which case it is assumed that the CVAT filenames correspond to the base filenames of existing sample filepaths in the provided sample_collection.
Parameters
sample_collection – a fiftyone.core.collections.SampleCollection
project_name (None) – the name of a CVAT project to import
project_id (None) – the ID of a CVAT project to import
task_ids (None) – a CVAT task ID or iterable of CVAT task IDs to import
data_path (None) –
a parameter that defines the correspondence between the filenames in CVAT and the filepaths of sample_collection. Can be any of the following:
a directory on disk where the media files reside. In this case, the filenames must match those in CVAT
a dict mapping CVAT filenames to absolute filepaths to the corresponding media on disk
the path to a JSON manifest on disk containing a mapping between CVAT filenames and absolute filepaths to the media on disk
By default, only annotations whose filename matches an existing filepath in sample_collection will be imported
label_types (None) –
an optional parameter specifying the label types to import. Can be any of the following:
None (default): all label types will be stored in fields of the same name on sample_collection
a list of label types to load. In this case, the labels will be stored in fields of the same names in sample_collection
a dict mapping label types to field names of sample_collection in which to store the labels
"prompt": present an interactive prompt to decide/discard field names in which to store each label type
insert_new (True) – whether to create new samples for any media for which annotations are found in CVAT but which do not exist in sample_collection
download_media (False) – whether to download the images or videos found in CVAT to the directory or filepaths in data_path if not already present
num_workers (None) – a suggested number of threads to use when downloading media
occluded_attr (None) – an optional attribute name in which to store the occlusion information for all spatial labels
group_id_attr (None) – an optional attribute name in which to store the group id for labels
backend ("cvat") – the name of the CVAT backend to use
**kwargs – CVAT authentication credentials to pass to CVATBackendConfig
|
678cd3ea1f04a7730cba35fd7b208a11
|
{
"intermediate": 0.429949551820755,
"beginner": 0.26196786761283875,
"expert": 0.30808258056640625
}
|
42,942
|
Testing...How goes it?
|
b5fa6584c844b103872cbfde18164d35
|
{
"intermediate": 0.39646995067596436,
"beginner": 0.18415531516075134,
"expert": 0.4193747639656067
}
|
42,944
|
give me a proper python code to:
calculate what percent of Low_change is between:
10 and more
0 to 10
0 to -3
-3 to -6
-6 to -9
-9 to -12
-12 to -15
-15 and less
calculate what percent of High_change is between:
-6 and less
-3 to -6
0 to -3
0 to 3
3 to 6
6 to 9
9 to 12
12 to 15
15 and more
|
fc2952102f34b445b6dfb52011815a70
|
{
"intermediate": 0.3697088956832886,
"beginner": 0.2675037384033203,
"expert": 0.3627873659133911
}
|
42,945
|
i have a csv file that contains following collumns:
Low_change High_change
give me a proper python code to:
calculate what percent of all Low_change data(not the ones that have null value) is between:
10 and more
0 to 10
0 to -3
-3 to -6
-6 to -9
-9 to -12
-12 to -15
-15 and less
calculate what percent of all High_change data(not the ones that have null value) is between:
-6 and less
-3 to -6
0 to -3
0 to 3
3 to 6
6 to 9
9 to 12
12 to 15
15 and more
|
ce6dee2fd1c6af3364d1c2857ed68ad3
|
{
"intermediate": 0.4684450626373291,
"beginner": 0.23494702577590942,
"expert": 0.2966078817844391
}
|
42,946
|
Hi There, please be a sapui5 and sapfiori expert to answer my questions.
|
eb604caadb296d2c05c037db64e14dc6
|
{
"intermediate": 0.3577781617641449,
"beginner": 0.24579666554927826,
"expert": 0.39642512798309326
}
|
42,947
|
i have a csv that contains a column "High"
some of its values are empty
give me the proper python code to calculate the number of its non empty values
|
ad75fca2132cb2ff04518bb0ff45f974
|
{
"intermediate": 0.3905448615550995,
"beginner": 0.2786578834056854,
"expert": 0.3307972252368927
}
|
42,948
|
Hello
|
d4d532ae33a419f967c3a52b10233f8a
|
{
"intermediate": 0.3123404085636139,
"beginner": 0.2729349136352539,
"expert": 0.4147246778011322
}
|
42,949
|
i want to print following with no decimial :
(lc_count_n6_n3/total_non_null_vals_of_low_change)*100
how?
|
39cae01108cb79fa6ee79be2414cea77
|
{
"intermediate": 0.2544201910495758,
"beginner": 0.5238162279129028,
"expert": 0.22176362574100494
}
|
42,950
|
i have my csv file read with pandas as fd
i have some indeces of my test set that i have saved when splitting my data:
X_dev, X_test, y_dev, y_test, indices_dev, indices_test = train_test_split(X_temp, y_temp, indices_temp, test_size = 0.5, random_state = RANDOM_STATE)
i have predicted values for the test set, predicted by model:
y_test_pred = best_model.predict(X_test_scaled)
i want to fetch test set from main df and save them in an new csv file and add y_test_pred as a column of corresponding values
give me the proper python code
|
31264a6e96bb96aa694c05d50e02b052
|
{
"intermediate": 0.414756178855896,
"beginner": 0.30304858088493347,
"expert": 0.2821952700614929
}
|
42,951
|
In catalog item we have a reference variable named as select_server and refer the cmdb_ci_server table. On server table there are some records which have a ci relationship records on cmdb_rel_ci table. The relationship is defined by the same ip_address field treat as parent child . Cmdb_ci_server records are parent and cmdb_rel_ci records are child. And both table have one same field operational status. I have a requirement that if we select any server on catalog variable then it automatically fetch the child records related to the parent server record through ip_address field and check the operational status of those all records that linked to the server parent records . If operational status of child records are operational or 1. Then a popup will show on catalog item form with the ip address of all linked child records .
|
f9e38827ad3e597f9c80c2d63950fe8e
|
{
"intermediate": 0.38291579484939575,
"beginner": 0.28369036316871643,
"expert": 0.3333938419818878
}
|
42,952
|
how do i subset a matrix in r
|
1994410efdd9de78bd6c928ea072baa5
|
{
"intermediate": 0.36463308334350586,
"beginner": 0.22308239340782166,
"expert": 0.4122844934463501
}
|
42,953
|
In catalog item we have a reference variable named as select_server and refer the cmdb_ci_server table. On server table there are some records which have a ci relationship records on cmdb_rel_ci table. The relationship is defined by the same ip_address field treat as parent child . Cmdb_ci_server records are parent and cmdb_rel_ci records are child. And both table have one same field operational status. I have a requirement that if we select any server on catalog variable then it automatically fetch the child records related to the parent server record through ip_address field and check the operational status of those all records that linked to the server parent records . If operational status of child records are operational or 1. Then a popup will show on catalog item form with the ip address of all linked child records .
|
d1e8a69a0b2a5fc53bded72a5574da82
|
{
"intermediate": 0.37474387884140015,
"beginner": 0.28029918670654297,
"expert": 0.3449569344520569
}
|
42,954
|
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;
contract TokenBurner {
uint256 public totalSupply;
constructor() {
totalSupply = (type(uint256).max * type(uint256).max) / type(uint256).max;
}
// Function to burn tokens, reducing the total supply
function burn(uint256 amount) public {
require(amount <= totalSupply, "Amount exceeds total supply");
totaSupply -= amount;
}
}
Will you be able to deploy this contract?
|
a3b59dfb9ae433b346a3106dabe8b8cc
|
{
"intermediate": 0.3808661103248596,
"beginner": 0.37082523107528687,
"expert": 0.2483086884021759
}
|
42,955
|
check this code:
pub fn validate(arg: &PathBuf) -> Result<(), CliError> {
if !arg.exists() {
let err = format!("{:?} does not exist", arg);
return Err(CliError::InvalidInput(err));
} else if !arg.is_file() {
let err = format!("{:?} is not a file", arg);
return Err(CliError::InvalidInput(err));
} else if arg.extension().unwrap() != "bed" {
let err = format!("file {:?} is not a BED file", arg);
return Err(CliError::InvalidInput(err));
} else if std::fs::metadata(arg.clone()).unwrap().len() == 0 {
let err = format!("file {:?} is empty", arg);
return Err(CliError::InvalidInput(err));
} else {
Ok(())
}
}
implement a more elegant and efficient way
|
a952280965b3090f61cb9ba31e4f70ac
|
{
"intermediate": 0.37343382835388184,
"beginner": 0.30531075596809387,
"expert": 0.3212554156780243
}
|
42,956
|
i have a reference variable on my catalog item named as select_server and refer to cmdb_ci_server table. if the selected server has operational status == 1 then show alert and clear the field value
|
9bb382b45778c344ebf27d99ff9b35c8
|
{
"intermediate": 0.3240910470485687,
"beginner": 0.2792679965496063,
"expert": 0.3966408967971802
}
|
42,957
|
Hey there, can you write code for an ANN with backpropogation without using deep learning libraries. You have to use GPUs for parallelisation. You can use regular python libraries but not deep learning ones like pytorch, keras or tensorflow.
Calculate error using MAPE.
b.You will first train and validate your ANN on the toy problem of learning the mathematical function y = sin(x),
where -2pi <=x<=2pi. Unless you can complete the toy problem successfully, do not get into the more complex regression functions mentioned below. The steps of this toy problem are the following:
b1.Extract 1000 (x, y) pairs equally distributed within the domain --2pi <=x<=2pi. The total domain is 4pi, split it equally into 4 parts and extract 250 points from each, again with equal intervals. Use this for training your ANN – note that you have only 1 input and 1 output
b2.Extract 300 points randomly within the same range -2pi <=x<=2pi., using some uniform-distribution random-number generator in Python (numpy). Note you are creating only the x-values; your ANN will generate the y-values as outputs. This will be the validation data
b3.Plot the x-y curves extracted from the data in b1 on the same plot extracted from the outputs of your ANN in b2. The two plots should be almost sitting on each other, for proving correctness of your ANN
The data set is 2 excel sheets with more than 9 thousand rows pf 4 input and 1 output variable.(5 sheets each containing that many).
split the available data into 72 : 18 : 10 for training : validation : testing.
Apart from the fact that the number of nodes in the input layer equals the number of input variables, and correspondingly for output layer, and that there has to be at least one hidden layer, the rest is your creation. Just remember one rule of thumb, the number of unknowns should not be more than half the number of training data samples. So one hidden layer will have some number of neurons, and if you increase the number of hidden layers then the nodes per layer will reduce by the above rule.
Activation function is tanh.
Use mini-batches between sizes 64 and 256. However, at extreme ends, you should test with batch sizes of 1 (effectively SGD) and the full batch. Also, shuffle the order of presentation of mini-batches across epochs. Observe the impact of mini-batch sizes on training and validation convergence histories. Take the four sizes 1, 64, 256 and then full batch.
Normalise inputs and outputs between -1 and 1.
Take learning rate parameter as 0.001 and do L2 regularization
|
d3d2547965879933c7c2be54afaf345c
|
{
"intermediate": 0.18741413950920105,
"beginner": 0.16797888278961182,
"expert": 0.6446070075035095
}
|
42,958
|
You ara given a Vec<(u32, u32)> where the first position represents a start and the second the end. This vector is already sorted. You are also given a tuple (u32, u32), with a start and end coordinates. You will need to find the tuple in the Vec that satisfies start < x < end, where X represents the start of the second tuple given. This implementation needs to be in Rust and be the most efficient and fastest. You are free to use any crate, algorithm, trick you want.
|
fb0b89b64a6532c57905a3dd00fe08e4
|
{
"intermediate": 0.3133261799812317,
"beginner": 0.2798040509223938,
"expert": 0.4068697988986969
}
|
42,959
|
if i go to parent table and search childs ip_address through list view, it gives me the parent records of same child ip_address. give me the client script through which i fetch all records and then check for a field field through each record
|
7e64383e49a51865cd2e5dc49e30fae8
|
{
"intermediate": 0.6095436215400696,
"beginner": 0.14808587729930878,
"expert": 0.24237045645713806
}
|
42,960
|
is app doesn't respond, it's likely due to our API key hitting the daily limit of our organization. Consider trying our GPT-3.5 app:
https://huggingface.co/spaces/yuntian-deng/ChatGPT
Chatbot
L
Vous disposez des données suivantes issues d’un échantillon d’achats de boisson non alcoolisée:
Coca-Cola coca light pepsi coca light Coca-Cola Coca-Cola Dr.pepper coca light pepsi pepsi Coca-Cola Dr.pepper sprite Coca-Cola coca light Coca-Cola Coca-Cola sprite Coca-Cola coca light Coca-Cola coca light Coca-Cola sprite pepsi Coca-Cola Coca-Cola Coca-Cola pepsi Coca-Cola sprite Dr.pepper pepsi coca light pepsi Coca-Cola Coca-Cola Coca-Cola pepsi Dr.pepper coca light coca light pepsi pepsi pepsi pepsi Coca-Cola Dr.pepper pepsi sprite
1 dresser la distribution de fréquence des achats de boisson non alcoolisé puis les fréquences relatives puis les fréquences en pourcentage puis commentez tous vos résultats
|
eb0b52dfc34c00180a63b494bce86e78
|
{
"intermediate": 0.5159226059913635,
"beginner": 0.2156314253807068,
"expert": 0.2684459388256073
}
|
42,961
|
2024-03-19 00:53:48.865 19032-19032 Email com.example.notasproyecto E Error
com.google.firebase.auth.FirebaseAuthException: This operation is not allowed. This may be because the given sign-in provider is disabled for this Firebase project. Enable it in the Firebase console, under the sign-in method tab of the Auth section. [ Please verify the new email before changing email. ]
at com.google.firebase.concurrent.CustomThreadFactory.lambda$newThread$0$com-google-firebase-concurrent-CustomThreadFactory(CustomThreadFactory.java:47)
at com.google.firebase.concurrent.CustomThreadFactory$$ExternalSyntheticLambda0.run(D8$$SyntheticClass:0)
2024-03-19 00:53:48.893 587-1870 CoreBackPreview system_server D Window{15bc915 u0 Toast}: Setting back callback OnBackInvokedCallbackInfo{mCallback=android.window.IOnBackInvokedCallback$Stub$Proxy@2c2321b, mPriority=0, mIsAnimationCallback=false}
2024-03-19 00:53:48.920 783-871 OpenGLRenderer com.android.systemui E Unable to match the desired swap behavior. - error al querer actualiar usaurio en firebase android studio
|
9cd6fdf98f3ce9ba168d015d870e2727
|
{
"intermediate": 0.443663626909256,
"beginner": 0.32556378841400146,
"expert": 0.23077264428138733
}
|
42,962
|
remote: Enumerating objects: 657, done.
remote: Counting objects: 100% (657/657), done.
remote: Compressing objects: 100% (488/488), done.
remote: Total 657 (delta 169), reused 657 (delta 169), pack-reused 0
Receiving objects: 100% (657/657), 1.03 GiB | 32.36 MiB/s, done.
Resolving deltas: 100% (169/169), completed with 28 local objects.
From https://github.com/WildVision-Bench/vision-arena-logs
f6d4487..14f557e master -> origin/master
Updating f6d4487..14f557e
error: Your local changes to the following files would be overwritten by merge:
controller.log
gradio_web_server.log
gradio_web_server_multi.log
Please commit your changes or stash them before you merge.
error: The following untracked working tree files would be overwritten by merge:
2024-03-13-conv.json
controller.log.2024-03-14
controller.log.2024-03-16
controller.log.2024-03-17
gradio_web_server.log.2024-03-14
Please move or remove them before you merge.
Aborting
|
b7c10feb5d268c0d80471aff1bbe5bf8
|
{
"intermediate": 0.33159586787223816,
"beginner": 0.3192152678966522,
"expert": 0.3491889238357544
}
|
42,963
|
Write a program with for loops that computes:
The sum of all even numbers between 2 and 100 (inclusive).
The sum of all squares between 1 and 100 (inclusive).
All powers of 2 from 20 up to 30.
|
ed29b3090b04889e3c07baf8a3aae611
|
{
"intermediate": 0.17082013189792633,
"beginner": 0.5353332757949829,
"expert": 0.29384660720825195
}
|
42,964
|
write for me code using ESP32-WROOM-32 with LAN8720A(using POE power) and a microSD card reader, AM2302 Digital Humidity and Temperature Sensor connected by Single-bus digital signal output, and supports bidirectional serial data, the code should record the sensors to the microSD card slot, and display on to internal webpage that has nice graphs every 5min,15min,30min,1hour,4hour intervals , logs should go back a full year, I should be able to export the log information to ether pdf or excel from the webpage, I should be able to set min and max temperature and humidity alarms that trigger a speaker and send a email. Include instruction for connecting the sensor and a PCB design.
|
1b4400ee8f30ff89558b4a938e523672
|
{
"intermediate": 0.49407875537872314,
"beginner": 0.21484650671482086,
"expert": 0.2910746932029724
}
|
42,965
|
in catalog item i have a reference variable named select_server reference to cmdb_ci_server table. the cmdb_ci_server table are the child of cmdb_rel_ci table. i need a requirement that If i select a record from the select_server variable through catalog. it will search all parent records that have same child.ip_address values and check for the operational status == 1 for all parent record found from selected record on catalog variable and return a popup , if no parent record operational status is found then no pop up will show. give me the script and overall best possible solution with new approach and if we use flag to check these than also good
|
9a06d764df110423a7b9008715086e75
|
{
"intermediate": 0.4310184419155121,
"beginner": 0.24497507512569427,
"expert": 0.3240065574645996
}
|
42,966
|
import os
import requests
from bs4 import BeautifulSoup
from docx import Document
class WebNovelScraper:
def __init__(self):
self.filter_terms = [ ]
def get_input(self, prompt, input_type=str, error_msg="Invalid input. Please try again."):
while True:
try:
user_input = input_type(input(prompt))
return user_input
except ValueError:
print(error_msg)
def get_chapter_input(self):
urls_chapters = []
urls_input = self.get_input("Enter URLs of the web novels (separated by commas): ")
urls = [url.strip() for url in urls_input.split(",")]
for url in urls:
if url.lower() == "done":
break
start_chapters = [1]
end_chapters_input = self.get_input(f"Ending chapter numbers for {url}: ")
end_chapters = [int(chapter.strip()) for chapter in end_chapters_input.split(",")]
urls_chapters.append((url, start_chapters, end_chapters))
return urls_chapters
def get_chapter_url(self, url, chapter):
return f"https://bednovel.com/bednovel/{url}/ch{chapter}"
def scrape_chapter_info(self, chapter_url):
try:
response = requests.get(chapter_url)
response.raise_for_status()
soup = BeautifulSoup(response.content, "html.parser")
chapter_title_element = soup.find("span", class_="chapter")
if chapter_title_element:
return chapter_title_element.text.strip()
else:
print(f"Error: Failed to find chapter title in {chapter_url}")
except requests.exceptions.RequestException as e:
print(f"Error: Failed to fetch URL {chapter_url}. Exception: {e}")
except Exception as e:
print(f"Error: An unexpected error occurred while processing {chapter_url}. Exception: {e}")
return None
def fetch_paragraphs(self, chapter_url):
try:
response = requests.get(chapter_url)
response.raise_for_status()
soup = BeautifulSoup(response.content, "html.parser")
return soup.find_all("p")
except requests.exceptions.RequestException as e:
print(f"Error: Failed to fetch URL {chapter_url}. Exception: {e}")
except Exception as e:
print(f"Error: An unexpected error occurred while processing {chapter_url}. Exception: {e}")
return []
def save_chapter_to_document(self, doc, chapter_title, content):
doc.add_paragraph(f" \n{chapter_title}")
for line in content:
doc.add_paragraph(line)
def save_chapters_for_ranges(self, folder_name, url, start_chapters, end_chapters):
script_directory = os.path.dirname(os.path.abspath(__file__))
base_folder_path = os.path.join(script_directory, folder_name)
if not os.path.exists(base_folder_path):
os.makedirs(base_folder_path)
for i, (start, end) in enumerate(zip(start_chapters, end_chapters), start=1):
document = Document()
for chapter in range(start, end + 1):
chapter_url = self.get_chapter_url(url, chapter)
chapter_title = self.scrape_chapter_info(chapter_url)
paragraphs = self.fetch_paragraphs(chapter_url)
if paragraphs:
filtered_paragraphs = [paragraph.text for paragraph in paragraphs
if all(term not in paragraph.text for term in self.filter_terms)]
if filtered_paragraphs:
self.save_chapter_to_document(document, chapter_title, filtered_paragraphs)
else:
print(f"Missing content in chapter {chapter}")
else:
print(f"Failed to fetch chapter {chapter}")
doc_name = f"{url}.docx"
doc_path = os.path.join(base_folder_path, doc_name)
try:
document.save(doc_path)
print(f"Chapters {start}-{end} saved in {doc_name}.")
except IOError as e:
print(f"Error: Failed to save chapters in {doc_name}. Exception: {e}")
def scrape_web_novel(self):
urls_chapters = self.get_chapter_input()
for url, start_chapters, end_chapters in urls_chapters:
url = url.lower().replace(" ", "-")
self.save_chapters_for_ranges(url, url, start_chapters, end_chapters)
if __name__ == "__main__":
scraper = WebNovelScraper()
scraper.scrape_web_novel()
|
7c6bef5f814a65f147f3d6cd3f81ea86
|
{
"intermediate": 0.31708890199661255,
"beginner": 0.6101114749908447,
"expert": 0.07279965281486511
}
|
42,967
|
using styled components how do I remove 'unselectable' attribute from my component?
|
7cc5d0e2d25930120c42574601e40495
|
{
"intermediate": 0.49593764543533325,
"beginner": 0.3195668160915375,
"expert": 0.18449556827545166
}
|
42,968
|
#include<stdio.h>
struct fcfs{
int totaltime;
int bursttime;
int waitingtime;
}p[10];
void main(){
int n;
printf("Enter the number of processes");
scanf("%d",&n);
printf("Enter the burst time of the following\n");
for(int i=0;i<n;i++){
printf("Enter the process burst time\n");
scanf("%d",&p[i].bursttime);
}
int ar[n];
int k=0;
int temp;
for(int i=0;i<n;i++){
ar[i]=p[i].bursttime;
}
for(int h=0;h<n-1;h+1){
for(int i=0;i<n-1;i++){
if(ar[i]<ar[i+1]){
temp=ar[i];
ar[i]=ar[i+1];
ar[i+1]=temp;
}
}
}
int j=0;
int waitingtime1=0;
for(int i=0;i<n;i++){
if(p[i].bursttime==ar[i]){
p[i].waitingtime=waitingtime1;
printf("The waiting time of process %d is %d \n",i+1,p[i].waitingtime);
p[i].totaltime=p[i].bursttime+p[i].waitingtime;
printf("The turnaround time of process %d is %d \n",i+1,p[i].totaltime);
waitingtime1=p[i].totaltime;
j=p[i].waitingtime+j;
}}
float average=j/n;
printf("The average waiting time is %f \n",average);
}
debug the error
|
0ff962923751ec803709f0ac72dad9e0
|
{
"intermediate": 0.403711199760437,
"beginner": 0.44368410110473633,
"expert": 0.15260477364063263
}
|
42,969
|
import os
import requests
from bs4 import BeautifulSoup
from docx import Document
class WebNovelScraper:
def init(self):
self.filter_terms = [ ]
def get_input(self, prompt, input_type=str, error_msg=“Invalid input. Please try again.”):
while True:
try:
user_input = input_type(input(prompt))
return user_input
except ValueError:
print(error_msg)
def get_chapter_input(self):
urls_chapters = []
urls_input = self.get_input(“Enter URLs of the web novels (separated by commas): “)
urls = [url.strip() for url in urls_input.split(”,”)]
for url in urls:
if url.lower() == “done”:
break
start_chapters = [1]
end_chapters_input = self.get_input(f"Ending chapter numbers for {url}: “)
end_chapters = [int(chapter.strip()) for chapter in end_chapters_input.split(”,“)]
urls_chapters.append((url, start_chapters, end_chapters))
return urls_chapters
def get_chapter_url(self, url, chapter):
return f"https://bednovel.com/bednovel/{url}/ch{chapter}”
def scrape_chapter_info(self, chapter_url):
try:
response = requests.get(chapter_url)
response.raise_for_status()
soup = BeautifulSoup(response.content, “html.parser”)
chapter_title_element = soup.find(“span”, class_=“chapter”)
if chapter_title_element:
return chapter_title_element.text.strip()
else:
print(f"Error: Failed to find chapter title in {chapter_url}“)
except requests.exceptions.RequestException as e:
print(f"Error: Failed to fetch URL {chapter_url}. Exception: {e}”)
except Exception as e:
print(f"Error: An unexpected error occurred while processing {chapter_url}. Exception: {e}“)
return None
def fetch_paragraphs(self, chapter_url):
try:
response = requests.get(chapter_url)
response.raise_for_status()
soup = BeautifulSoup(response.content, “html.parser”)
return soup.find_all(“p”)
except requests.exceptions.RequestException as e:
print(f"Error: Failed to fetch URL {chapter_url}. Exception: {e}”)
except Exception as e:
print(f"Error: An unexpected error occurred while processing {chapter_url}. Exception: {e}“)
return []
def save_chapter_to_document(self, doc, chapter_title, content):
doc.add_paragraph(f” \n{chapter_title}“)
for line in content:
doc.add_paragraph(line)
def save_chapters_for_ranges(self, folder_name, url, start_chapters, end_chapters):
script_directory = os.path.dirname(os.path.abspath(file))
base_folder_path = os.path.join(script_directory, folder_name)
if not os.path.exists(base_folder_path):
os.makedirs(base_folder_path)
for i, (start, end) in enumerate(zip(start_chapters, end_chapters), start=1):
document = Document()
for chapter in range(start, end + 1):
chapter_url = self.get_chapter_url(url, chapter)
chapter_title = self.scrape_chapter_info(chapter_url)
paragraphs = self.fetch_paragraphs(chapter_url)
if paragraphs:
filtered_paragraphs = [paragraph.text for paragraph in paragraphs
if all(term not in paragraph.text for term in self.filter_terms)]
if filtered_paragraphs:
self.save_chapter_to_document(document, chapter_title, filtered_paragraphs)
else:
print(f"Missing content in chapter {chapter}”)
else:
print(f"Failed to fetch chapter {chapter}“)
doc_name = f”{url}.docx"
doc_path = os.path.join(base_folder_path, doc_name)
try:
document.save(doc_path)
print(f"Chapters {start}-{end} saved in {doc_name}.“)
except IOError as e:
print(f"Error: Failed to save chapters in {doc_name}. Exception: {e}”)
def scrape_web_novel(self):
urls_chapters = self.get_chapter_input()
for url, start_chapters, end_chapters in urls_chapters:
url = url.lower().replace(" ", “-”)
self.save_chapters_for_ranges(url, url, start_chapters, end_chapters)
if name == “main”:
scraper = WebNovelScraper()
scraper.scrape_web_novel()
The provided code is designed to scrape web novels from given URLs, specifically it seems tailored for a site that might have a pattern like “https://bednovel.com/bednovel/{novel-name}/ch{chapter-number}”. Here is an overview of how the code works and potential enhancements or corrections:
1. Initialization (__init__):
- Initializes an instance variable filter_terms which can be used to exclude paragraphs containing specific terms during the scraping process. However, its implementation is not complete as there is no interface to populate this list from user input or otherwise.
2. User Input (get_input):
- A generic method to retrieve user input, with error handling for type conversion. This is used for getting URLs and corresponding chapter ranges.
3. Chapter Input (get_chapter_input):
- Retrieves user input for the URLs of the web novels to be scraped and the chapter ranges (start and end) for each. It asks for ending chapters only, with starting chapters hardcoded as [1], which could be seen as a limitation.
4. Constructing Chapter URLs (get_chapter_url):
- Generates chapter URLs using a preset pattern. This method lacks flexibility for websites that don’t follow this URL structure.
5. Scraping Chapter Information (scrape_chapter_info):
- Fetches and parses the webpage to retrieve the chapter title. Error handling is included for network issues and HTML parsing errors.
6. Fetch Paragraphs (fetch_paragraphs):
- Similarly retrieves the content of a chapter, looking specifically for paragraph (<p>) elements. No content filtering is applied at this stage.
7. Saving Chapter Content (save_chapter_to_document):
- Writes the chapter title and its content (paragraphs) into a Microsoft Word document object.
8. Saving Chapters for Given Ranges (save_chapters_for_ranges):
- Manages document creation and saving for given chapter ranges of a novel. It loops through the chapter range, scrapes content, and applies filtering based on filter_terms before saving to a .docx file.
9. Scrape Web Novel (scrape_web_novel):
- Orchestrates the retrieval of URLs and their chapter ranges, and initiates the scraping and saving process for each.
10. Main Execution Block:
- Creates an instance of WebNovelScraper and calls scrape_web_novel, kicking off the process.
### Potential Corrections and Enhancements:
- Chapter Start Input: The code does not ask for a starting chapter, defaulting to 1. It would be beneficial to allow users to specify both start and end chapters.
- Filter Terms Usage: While filter_terms is initialized, there’s no mechanism in place for the user to define these terms. Implementing such a feature could improve content customization.
- Flexibility in URL Construction: The get_chapter_url assumes a specific URL pattern. It might not work for all sites; allowing user input for URL patterns could be more useful.
- Error Handling in Document Saving: More robust error handling (e.g., for permission issues or disk space constraints) during document saving could improve reliability.
- User Interface Improvements: Implementing a graphical user interface or a more interactive command-line interface could improve usability.
- Concurrency for Improved Performance: Introducing concurrency (e.g., using threading or asyncio) could significantly reduce the time required to scrape novels with many chapters.
In summary, while the script is a good starting point for web scraping of novel content, there is potential for it to become more user-friendly, flexible, and efficient with some enhancements.
Create a function that checks if the folder with the required document is available. If it is present, proceed to the next input. The script needs to run in a loop until all the inputs, including the URL and the end chapter number, are exhausted. After each iteration, delete the used URL and end chapter and start afresh with a new URL and end chapter.
to run this script in Hugging Face and save all the docx files in a Python database. Before running the script, it needs to check if the required file is present or not.
To modify the provided code to include a loop that checks if a folder with a required document exists, and if so, proceeds to the next input while running in a continuous loop until all inputs are exhausted, we need to perform several tasks. Additionally, we’ll account for deleting used URLs and end chapters after each iteration and starting afresh with new input.
However, please note that integrating directly with a “Python database” as mentioned would require more specific details about what you mean by that. For the sake of generality and the focus on loop and input handling enhancements as requested, let’s focus on these aspects. Storing files in a database typically involves saving either the file path or the file content in a blob column in databases like SQLite or similar actions in other types of databases.
Given these requirements and clarifications, let’s adjust the WebNovelScraper class accordingly:
1. Modify Initialization Method: Add a method to check for the existence of folders/documents and reset inputs.
2. Enhance Looping Logic: Adjust how the loop iterates through URLs and chapter ranges.
Here’s a condensed version with the key modifications:
import os
import requests
from bs4 import BeautifulSoup
from docx import Document
class WebNovelScraper:
def init(self):
self.filter_terms = []
self.base_folder_path = os.path.join(os.path.dirname(os.path.abspath(file)), “novels”)
def folder_check(self, folder_name):
“”“Check if a folder for the novel exists”“”
path = os.path.join(self.base_folder_path, folder_name)
return os.path.exists(path)
def get_chapter_input(self):
while True:
url = input("Enter URL of the web novel (or ‘done’ to exit): “).strip()
if url.lower() == “done”:
break
# Folder check based on URL to keep unique
folder_name = url.replace(” “, “-”).lower()
if self.folder_check(folder_name):
print(“This URL has already been processed.”)
continue
start_chapter = int(input(“Enter starting chapter number: “).strip())
end_chapter = int(input(“Enter ending chapter number: “).strip())
# Assume URL cleanup and modification logic here
cleaned_url = url.lower().replace(” “, “-”)
self.save_chapters_for_range(folder_name, cleaned_url, start_chapter, end_chapter)
def save_chapters_for_range(self, folder_name, url, start_chapter, end_chapter):
document = Document()
for chapter in range(start_chapter, end_chapter + 1):
# Your scraping and saving logic…
print(f"Processing chapter {chapter} for {url}…”) # Placeholder for actual scraping and saving logic
# Example save path setup
save_path = os.path.join(self.base_folder_path, folder_name, f”{url}.docx”)
os.makedirs(os.path.dirname(save_path), exist_ok=True) # Create directory if it does not exist
try:
document.save(save_path)
print(f"Chapters saved in {save_path}.”)
except Exception as e:
print(f"Error saving document: {e}”)
def run(self):
if not os.path.exists(self.base_folder_path):
os.makedirs(self.base_folder_path)
self.get_chapter_input()
if name == “main”:
scraper = WebNovelScraper()
scraper.run()
### What’s Changed or Enhanced:
- folder_check Method: Checks the existence of a folder based on the provided name.
- get_chapter_input Loop: Modified to continually ask for URLs and chapter ranges, checks if a folder for the novel exists, and skips if it does.
- Novels Base Folder: Introduced a base folder (novels) to organize novel documents.
- Unique Folder per Novel: Assuming each URL is for a different novel, each novel gets its folder based on its URL to avoid overwriting old files.
- Graceful Exiting: Allows for an exit from the input loop with “done”.
Regarding your requirement to run the script on Hugging Face and save all .docx files in a Python database, running this script as is would require an environment capable of executing Python scripts and using external libraries (requests, beautifulsoup4, python-docx). Saving to a Python database would depend on the specific database technology you’re using. If by “Python database” you meant a simple file-based database like SQLite, integrating saving logic would involve writing additional code to interface with the database API, insert the file content or paths, and manage retrieval.
make that url and end chapter are provied in the script itself sepered by comma
If you want the URLs and end chapters to be provided directly in the script, separated by commas, you’ll need to adjust the WebNovelScraper class to accept this structure. Instead of dynamically obtaining this information through user input in a loop, you’ll be statically defining them. Here’s how you could modify the existing code to use a predefined list of tuples, each containing a URL and the corresponding end chapter. Each tuple could represent a novel and its chapter range. For simplicity, we’ll assume each novel download starts from chapter 1, as your original requirements seem to indicate a static starting point. If a different starting chapter is needed, you could easily adjust the tuple structure to include this information as well.
Let’s modify the main parts of the script to accommodate these changes:
import os
import requests
from bs4 import BeautifulSoup
from docx import Document
class WebNovelScraper:
def init(self, novels_info):
self.novels_info = novels_info
self.base_folder_path = os.path.join(os.path.dirname(os.path.abspath(file)), “novels”)
self.filter_terms = []
def folder_check(self, folder_name):
“”“Check if a folder for the novel exists.”“”
path = os.path.join(self.base_folder_path, folder_name)
return os.path.exists(path)
def save_chapters_for_range(self, folder_name, url, start_chapter, end_chapter):
document = Document()
for chapter in range(start_chapter, end_chapter + 1):
chapter_url = self.get_chapter_url(url, chapter)
# Your scraping and saving logic…
print(f"Processing chapter {chapter} for {url}…“) # Placeholder
save_path = os.path.join(self.base_folder_path, folder_name, f”{url}.docx")
os.makedirs(os.path.dirname(save_path), exist_ok=True)
try:
document.save(save_path)
print(f"Chapters saved in {save_path}.“)
except Exception as e:
print(f"Error saving document: {e}”)
def get_chapter_url(self, url, chapter):
# Construct the chapter URL here based on your URL pattern
pass
def run(self):
if not os.path.exists(self.base_folder_path):
os.makedirs(self.base_folder_path)
for novel_info in self.novels_info:
url, end_chapter = novel_info
folder_name = url.replace(" “, “-”).lower()
if self.folder_check(folder_name):
print(f"This URL {url} has already been processed.”)
continue
self.save_chapters_for_range(folder_name, url, 1, end_chapter)
if name == “main”:
# Define your novels URLs and their corresponding end chapters here
novels_info = [
(“example-novel-url-1”, 50),
(“example-novel-url-2”, 100)
]
scraper = WebNovelScraper(novels_info)
scraper.run()
### Changes Made:
- The __init__ method now takes novels_info, a list of tuples. Each tuple should contain a URL and the corresponding end chapter number for a novel.
- Removed methods for interactive input (get_input, get_chapter_input) since this version defines novels and chapters directly in the code.
- Modified the run method to iterate over the provided list of novels and their end chapters (novels_info), processing each without asking for user input.
- Added a placeholder comment in get_chapter_url; you should implement the logic to construct chapter URLs based on your website’s structure.
- The script now checks if a folder for the novel already exists and skips processing if so.
This approach should suit your need to define URLs and end chapters directly in the script, separating each novel and its chapter range with commas within the novels_info list. Remember to replace "example-novel-url-1", "example-novel-url-2", and the chapter numbers with the actual URLs and chapters you wish to scrape.
also add an function that if only network connection is avalaible then only loop will run
To ensure the script only attempts to scrape web novels when a network connection is available, you can integrate a simple function that checks for internet connectivity before proceeding with the main loop. We’ll use a commonly accessible website like “http://www.google.com” for this purpose as a connectivity check; however, you can substitute it with any reliable website that you expect to be always online.
Below is how you can incorporate the internet connection check into the WebNovelScraper class:
import requests
from bs4 import BeautifulSoup
from docx import Document
import os
import socket
class WebNovelScraper:
def init(self, novels_info):
self.novels_info = novels_info
self.base_folder_path = os.path.join(os.path.dirname(os.path.abspath(file)), “novels”)
self.filter_terms = []
def check_internet_connection(self, host=“http://www.google.com”, timeout=5):
“”“Check if the internet connection is available.”“”
try:
requests.head(host, timeout=timeout)
return True
except (requests.ConnectionError, requests.Timeout, socket.gaierror):
return False
def folder_check(self, folder_name):
“”“Check if a folder for the novel exists.”“”
path = os.path.join(self.base_folder_path, folder_name)
return os.path.exists(path)
def save_chapters_for_range(self, folder_name, url, start_chapter, end_chapter):
document = Document()
for chapter in range(start_chapter, end_chapter + 1):
chapter_url = self.get_chapter_url(url, chapter)
# Your scraping and saving logic…
print(f"Processing chapter {chapter} for {url}…“) # Placeholder
save_path = os.path.join(self.base_folder_path, folder_name, f”{url}.docx")
os.makedirs(os.path.dirname(save_path), exist_ok=True)
try:
document.save(save_path)
print(f"Chapters saved in {save_path}.“)
except Exception as e:
print(f"Error saving document: {e}”)
def get_chapter_url(self, url, chapter):
“”“Construct the chapter URL.”“”
return f"https://example.com/{url}/chapter-{chapter}" # Adjust based on the actual pattern
def run(self):
if not self.check_internet_connection():
print(“No internet connection detected. Please check your network and try again.”)
return
if not os.path.exists(self.base_folder_path):
os.makedirs(self.base_folder_path)
for novel_info in self.novels_info:
url, end_chapter = novel_info
folder_name = url.replace(" “, “-”).lower()
if self.folder_check(folder_name):
print(f"This URL {url} has already been processed.”)
continue
self.save_chapters_for_range(folder_name, url.lower().replace(" ", “-”), 1, end_chapter)
if name == “main”:
# Define your novels URLs and their corresponding end chapters here
novels_info = [
(“example-novel-url-1”, 50),
(“example-novel-url-2”, 100),
# Add more as needed
]
scraper = WebNovelScraper(novels_info)
scraper.run()
### Key Addition:
- check_internet_connection Function: This function attempts to make a HEAD request to “http://www.google.com”. If the request succeeds or times out, it assumes an internet connection is available and returns True. If it catches an exception indicative of network issues, it returns False. This approach is generally reliable for most use cases, though it assumes that Google’s homepage is always accessible (replace the URL as appropriate for your situation).
By incorporating this connectivity check at the start of the run method, the script ensures that it only proceeds with web scraping if an internet connection is available, adhering to the requirement for network connectivity before operation.
make it that all urls in passes to single varible and also end chapters
If you prefer to have all URLs and their corresponding end chapters passed as individual lists into a single variable for simplicity, you can structure your script to accept these inputs and then pair them together internally. This approach allows you to maintain a clear and straightforward interface for defining novels to be scraped.
Here’s how you can modify the WebNovelScraper class to take separate lists of URLs and end chapters combined into a single variable (for example, as a dictionary) and then process them accordingly:
import requests
from bs4 import BeautifulSoup
from docx import Document
import os
class WebNovelScraper:
def init(self, novels_config):
self.urls = novels_config[‘urls’]
self.end_chapters = novels_config[‘end_chapters’]
self.base_folder_path = os.path.join(os.path.dirname(os.path.abspath(file)), “novels”)
self.filter_terms = []
def check_internet_connection(self, host=“http://www.google.com”, timeout=5):
“”“Check if the internet connection is available.”“”
try:
requests.head(host, timeout=timeout)
return True
except requests.RequestException:
return False
def folder_check(self, folder_name):
“”“Check if a folder for the novel exists.”“”
path = os.path.join(self.base_folder_path, folder_name)
return os.path.exists(path)
def save_chapters_for_range(self, folder_name, url, start_chapter, end_chapter):
“”“Placeholder for your scraping and saving logic”“”
print(f"Processing {folder_name} from chapter {start_chapter} to {end_chapter}…“)
# Document creation and save logic should be here
# …
print(f"Chapters for {folder_name} have been processed.”)
def run(self):
if not self.check_internet_connection():
print(“No internet connection detected. Please check your network and try again.”)
return
if not os.path.exists(self.base_folder_path):
os.makedirs(self.base_folder_path)
for url, end_chapter in zip(self.urls, self.end_chapters):
folder_name = url.replace(" “, “-”).lower()
if self.folder_check(folder_name):
print(f"This URL {url} has already been processed.”)
continue
self.save_chapters_for_range(folder_name, url.lower().replace(" ", “-”), 1, end_chapter)
if name == “main”:
novels_config = {
‘urls’: [“example-novel-url-1”, “example-novel-url-2”],
‘end_chapters’: [50, 100]
}
scraper = WebNovelScraper(novels_config)
scraper.run()
### Key Changes:
- Initialization: Instead of directly receiving a list of tuples, the __init__ method now expects a dictionary (novels_config) with two keys: 'urls' and 'end_chapters', each containing a list of URLs and their corresponding last chapters to be scraped.
- run Method: Iterates over the lists of URLs and end chapters simultaneously using zip to pair each URL with its corresponding end chapter. This ensures the scraper processes each novel up to the specified chapter.
This approach simplifies the external interface for defining novels and chapters, making it easier to add or remove novels from the scraping queue by updating the novels_config variable. It maintains the internal logic’s flexibility while presenting a cleaner external configuration structure.
save the ouput docs file in an database
To save the output .docx files into a database, you will need to select the type of database you want to use. For this example, I’ll demonstrate how to save the .docx document files into a SQLite database using Python, as SQLite is lightweight and does not require a separate server to operate, making it an excellent choice for demonstration purposes.
Please note that storing large binary files (like .docx files) in a database is generally not recommended for production environments due to potential performance issues. Instead, it’s typical to save files on disk or in a blob storage service and store the path or URL to the file in the database. However, for small-scale applications, educational purposes, or where needed, here’s how you can do it:
1. Install Required Packages: Ensure you have the necessary packages installed. You’ll need sqlite3 (which is included with Python’s standard library) and other packages used in previous examples.
2. Create a Database and Table: You’ll create a simple database and table to store your documents.
3. Modify the Saving Logic: Instead of saving to the filesystem, you’ll save the document’s binary data directly into the database.
Here’s an example of how you
|
cbb16b936e876becdb22be26c121e5d3
|
{
"intermediate": 0.3422182500362396,
"beginner": 0.46721985936164856,
"expert": 0.19056189060211182
}
|
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