row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
8,129
|
List<List<int>> subsequenceGenerator(int seq, int sub) {
List<List<int>> cache = [];
List<List<int>> result = [];
for (int i = 0; i < seq; i++) {
List<List<int>> newSub = [];
// Adding the current element to the existing subsequences in the cache
for (List<int> subList in cache) {
if (subList.length < sub) {
newSub.add([...subList, i]);
}
}
// Adding the current element as a new subsequence
newSub.add([i]);
// Adding new subsequences to the result list
for (List<int> subList in newSub) {
result.add(subList);
}
// Caching the new subsequences
cache.addAll(newSub);
}
return result..retainWhere((e) => e.length == sub);
}
|
04628b90431492ca585a0f6096e9220a
|
{
"intermediate": 0.3484320640563965,
"beginner": 0.3668258786201477,
"expert": 0.2847420573234558
}
|
8,130
|
Code cookie clicker
|
32d4215fd16f86c43b60074a198c642d
|
{
"intermediate": 0.2472628504037857,
"beginner": 0.40211325883865356,
"expert": 0.3506239354610443
}
|
8,131
|
package boardgame.model;
import boardgame.util.GameResult;
import com.fasterxml.jackson.core.type.TypeReference;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.fasterxml.jackson.datatype.jsr310.JavaTimeModule;
import javafx.beans.property.ObjectProperty;
import javafx.beans.property.SimpleObjectProperty;
import java.io.*;
import java.time.LocalDateTime;
import java.util.ArrayList;
import java.util.List;
/**
* The {@code BoardGameModel} class represents a board game model.
* It manages the game logic, board state, and player moves.
*/
public class BoardGameModel {
private File file = new File("game_result.json");
private ObjectMapper mapper = new ObjectMapper().registerModule(new JavaTimeModule());
private GameResult result = new GameResult();
private String player1;
private String player2;
private static final int BOARD_SIZE = 5;
private static final int WINNING_CONDITION = 3;
private boolean gameEnded;
private Square[][] board;
private ObjectProperty<Square>[][] squareProperties;
private ObjectProperty<Square> currentPlayer;
/**
* Constructs a new {@code BoardGameModel} object.
* Initializes the board, square properties, and the current player.
*/
public BoardGameModel() {
board = new Square[BOARD_SIZE][BOARD_SIZE];
squareProperties = new ObjectProperty[BOARD_SIZE][BOARD_SIZE];
currentPlayer = new SimpleObjectProperty<>(Square.RED);
initializeBoard();
initializeSquareProperties();
}
/**
* Writes the game result to JSON.
*
* @param result the game result to be written
* @throws IOException if an error occurs while writing to JSON
*/
private void writeToJson(GameResult result) throws IOException {
result.setWinner(currentPlayer.get() == Square.RED ? player1 : player2);
result.setPlayer1Name(player1);
result.setPlayer2Name(player2);
List<GameResult> resultList = new ArrayList<>();
if (file.exists()) {
try (FileReader reader = new FileReader(file)) {
resultList = mapper.readValue(reader, new TypeReference<ArrayList<GameResult>>() {
});
}
}
resultList.add(result);
try (FileWriter writer = new FileWriter(file)) {
mapper.writeValue(writer, resultList);
}
}
/**
* Sets the names of the players.
*
* @param name1 the name of player 1
* @param name2 the name of player 2
*/
public void setNames(String name1, String name2) {
player1 = name1;
player2 = name2;
}
/**
* Initializes the game board with empty squares.
*/
private void initializeBoard() {
for (int i = 0; i < BOARD_SIZE; i++) {
for (int j = 0; j < BOARD_SIZE; j++) {
board[i][j] = Square.NONE;
}
}
result.setStartDate(LocalDateTime.now());
}
@SuppressWarnings("unchecked")
private void initializeSquareProperties() {
for (int i = 0; i < BOARD_SIZE; i++) {
for (int j = 0; j < BOARD_SIZE; j++) {
squareProperties[i][j] = new SimpleObjectProperty<>(Square.NONE);
}
}
}
/**
* Makes a move on the game board at the specified row and column.
*
* @param row the row index
* @param col the column index
*/
public void makeMove(int row, int col) {
try {
if (!gameEnded && isValidMove(row, col)) {
board[row][col] = currentPlayer.get();
squareProperties[row][col].set(board[row][col]);
if (currentPlayer.get() == Square.RED) {
result.player1count++;
} else {
result.player2count++;
}
if (checkWin()) {
System.out.println("Player " + currentPlayer.get() + " wins!");
gameEnded = true;
result.setEndDate(LocalDateTime.now());
} else if (checkDraw()) {
System.out.println("It's a draw!");
gameEnded = true;
result.setEndDate(LocalDateTime.now());
} else {
currentPlayer.set(currentPlayer.get() == Square.RED ? Square.BLUE : Square.RED);
}
if (gameEnded) {
writeToJson(result);
}
}
} catch (IOException e) {
System.err.println("An error occurred while writing to JSON: " + e.getMessage());
}
}
private boolean isValidMove(int row, int col) {
return row >= 0 && row < BOARD_SIZE && col >= 0 && col < BOARD_SIZE && board[row][col] == Square.NONE;
}
/**
* Checks if a player has won the game.
*
* @return {@code true} if a player has won, {@code false} otherwise
*/
public boolean checkWin() {
return checkRows() || checkColumns() || checkDiagonals();
}
private boolean checkRows() {
for (int i = 0; i < BOARD_SIZE; i++) {
int redCount = 0;
for (int j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == Square.RED) {
redCount++;
if (redCount >= WINNING_CONDITION) {
return true;
}
} else {
redCount = 0;
}
}
}
return false;
}
private boolean checkColumns() {
for (int j = 0; j < BOARD_SIZE; j++) {
int redCount = 0;
for (int i = 0; i < BOARD_SIZE; i++) {
if (board[i][j] == Square.RED) {
redCount++;
if (redCount >= WINNING_CONDITION) {
return true;
}
} else {
redCount = 0;
}
}
}
return false;
}
private boolean checkDiagonals() {
for (int i = 0; i < BOARD_SIZE; i++) {
for (int j = 0; j < BOARD_SIZE; j++) {
if (checkDiagonalFromPosition(i, j)) {
return true;
}
}
}
return false;
}
private boolean checkDiagonalFromPosition(int row, int col) {
// Check diagonal from top-left to bottom-right
if (checkDiagonalFromTopLeft(row, col)) {
return true;
}
// Check diagonal from top-right to bottom-left
if (checkDiagonalFromTopRight(row, col)) {
return true;
}
return false;
}
private boolean checkDiagonalFromTopLeft(int startRow, int startCol) {
if (startRow + WINNING_CONDITION > BOARD_SIZE || startCol + WINNING_CONDITION > BOARD_SIZE) {
return false;
}
int redCount = 0;
for (int i = 0; i < WINNING_CONDITION; i++) {
if (board[startRow + i][startCol + i] == Square.RED) {
redCount++;
} else {
break;
}
}
return redCount >= WINNING_CONDITION;
}
private boolean checkDiagonalFromTopRight(int startRow, int startCol) {
if (startRow + WINNING_CONDITION > BOARD_SIZE || startCol - WINNING_CONDITION + 1 < 0) {
return false;
}
int redCount = 0;
for (int i = 0; i < WINNING_CONDITION; i++) {
if (board[startRow + i][startCol - i] == Square.RED) {
redCount++;
} else {
break;
}
}
return redCount >= WINNING_CONDITION;
}
/**
* Checks if the game is a draw (all squares are filled).
*
* @return {@code true} if the game is a draw, {@code false} otherwise
*/
public boolean checkDraw() {
for (int i = 0; i < BOARD_SIZE; i++) {
for (int j = 0; j < BOARD_SIZE; j++) {
if (board[i][j] == Square.NONE) {
return false;
}
}
}
return true;
}
/**
* Returns the square property at the specified row and column.
*
* @param row the row index
* @param col the column index
* @return the square property at the specified row and column
*/
public ObjectProperty<Square> squareProperty(int row, int col) {
return squareProperties[row][col];
}
/**
* Resets the game board to its initial state.
*/
public void resetBoard() {
gameEnded = false;
initializeBoard();
initializeSquareProperties();
currentPlayer.set(Square.RED);
}
/**
* Returns the current player.
*
* @return the current player
*/
public Square getCurrentPlayer() {
return currentPlayer.get();
}
}
okay you see there is board that 5x5 which is like [0, 0], [0, 1], [0, 2], [0, 3], [0,4]; [1, 0], [1 1], [1, 2], [1, 3], [1,4]; [2, 0], [2, 1], [2, 2], [2, 3], [2,4]; [3, 0], [3, 1], [3, 2], [3, 3], [3,4]; [4, 0], [4, 1], [4, 2], [4, 3], [4, 4]. even when red stone place on [0, 0], [2, 2], [4,4] or [0, 2], [2, 2], [4, 2] or [2, 0], [2, 2], [2, 4] or [0, 4], [2, 2], [4, 0], or [4, 0], [4, 2], [4, 4] it is considered winning. he game is over when a row, a column, or a diagonal contains 3 stones of the same color. (You must consider all possible diagonals, not only the main diagonal and the antidiagonal). The three stones do not have to be adjacent. The player to which these stones belong loses the game. Don't need to be ADJACENT!!! Give me full developed code
|
4ec354fdd004f1785b5058d8f5f48a08
|
{
"intermediate": 0.3211197555065155,
"beginner": 0.5056875944137573,
"expert": 0.17319262027740479
}
|
8,132
|
Hello
|
25ce4243fb60fda0299e2c78b78daaff
|
{
"intermediate": 0.3123404085636139,
"beginner": 0.2729349136352539,
"expert": 0.4147246778011322
}
|
8,133
|
hi
|
cc5914d218918500eff54d1a34529a30
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
8,134
|
how do i add a favicon to my html site
|
07335d508db2fea71b28414b85313e46
|
{
"intermediate": 0.39619889855384827,
"beginner": 0.36839038133621216,
"expert": 0.23541074991226196
}
|
8,135
|
hi
|
291577f2af010f119908622b332f193d
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
8,136
|
Write a bash script to output the date
|
0f51da9d28eb1cbc8cc49d87ab5c6152
|
{
"intermediate": 0.41736847162246704,
"beginner": 0.24449226260185242,
"expert": 0.33813923597335815
}
|
8,137
|
How do I add a favicon to my site
|
486e275104f3d4296c8e791f6ae4d9d4
|
{
"intermediate": 0.34656795859336853,
"beginner": 0.32479554414749146,
"expert": 0.3286365866661072
}
|
8,138
|
Write a bash script to output the date
|
e5d2a20d8b3038cf503f8113b94fa9b0
|
{
"intermediate": 0.41736847162246704,
"beginner": 0.24449226260185242,
"expert": 0.33813923597335815
}
|
8,139
|
Change the code below so that the function url def get_newly_created_contracts(start_block, end_block): returns the addresses that are in the Trasactions tab and not the internal txns
import requests
bscscan_api_key = 'CXTB4IUT31N836G93ZI3YQBEWBQEGGH5QS'
def get_newly_created_contracts(start_block, end_block):
url = f'https://api.bscscan.com/api?module=account&action=txlistinternal&startblock={start_block}&endblock={end_block}&sort=asc&apikey={bscscan_api_key}'
try:
response = requests.get(url)
response.raise_for_status()
except requests.exceptions.RequestException as e:
print(f'Error in API request: {e}')
return []
data = response.json()
if data['status'] == '0':
print(f"Error: {data['result']}")
return []
return [tx for tx in data['result'] if tx['isError'] == '0' and tx['contractAddress'] != '']
def display_new_contracts(start_block, end_block):
contracts = get_newly_created_contracts(start_block, end_block)
if not contracts:
print('No new contracts found.')
else:
print(f'Newly created smart contracts between blocks {start_block} and {end_block}: ')
for contract in contracts:
print(f"Block: {contract['blockNumber']} - Address: {contract['contractAddress']}")
start_block = 28504585 # Replace with your desired start block
end_block = 28504585 # Replace with your desired end block
display_new_contracts(start_block, end_block)
|
e74acb57c3961dd4fd2896f33b111c6e
|
{
"intermediate": 0.4758303761482239,
"beginner": 0.36968833208084106,
"expert": 0.15448130667209625
}
|
8,140
|
from kivy.app import App
from kivy.uix.screenmanager import ScreenManager, Screen
from kivy.uix.button import Button
from kivy.uix.image import Image
from kivy.uix.boxlayout import BoxLayout
from kivy.uix.behaviors import ButtonBehavior
from kivy.graphics.texture import Texture
from kivy.clock import Clock
from kivy.metrics import dp
from kivy.properties import StringProperty
import JsonUtl as jsonutil
import cv2
import threading
class ButtonImage(ButtonBehavior, Image):
pass
class MainScreen(Screen):
ServerListVar = jsonutil.OpenJsontoArray()
BtnId = 0
DelBtnId = 0
def __init__(self, **kw):
super().__init__(**kw)
for i in range (0,len(self.ServerListVar)):
self.CreateButtons(i)
def Clicked(self,widget):
Adress = self.ids.StreamAdress
if not Adress.text == "":
self.ServerListVar.append(Adress.text.strip())
jsonutil.SaveArraytoJson(self.ServerListVar)
self.CreateButtons(len(self.ServerListVar)- 1)
Adress.text = ""
def CamSelectButtonPressed(self,x):
app = App.get_running_app()
app.data = self.ServerListVar[x]
app.root.current = "Stream"
def CamDeleteButtonPressed(self,x):
del self.ServerListVar[x]
self.ids.CamSelectStackLayout.clear_widgets()
jsonutil.SaveArraytoJson(self.ServerListVar)
self.ServerListVar = jsonutil.OpenJsontoArray()
self.BtnId = 0
self.DelBtnId = 0
for i in range (0,len(self.ServerListVar)):
self.CreateButtons(i)
def CreateButtons(self, i):
box = BoxLayout(size_hint=(None, None), width=dp(200), height=dp(200), orientation="vertical")
bimage = ButtonImage(source = "ConnectionError.png")
b2 = ButtonImage(size_hint=(None, None), width=dp(200), height=dp(50))
b2.source = "DeleteButton.png"
bimage.bind(on_press=lambda instance, button_id=self.BtnId: self.CamSelectButtonPressed(button_id))
self.BtnId = self.BtnId + 1
b2.bind(on_press=lambda instance, button_id=self.DelBtnId: self.CamDeleteButtonPressed(button_id))
self.DelBtnId = self.DelBtnId + 1
# call capture in a separate thread
threading.Thread(target=self.capture_video, args=(self.ServerListVar[i], bimage)).start()
box.add_widget(bimage)
box.add_widget(b2)
self.ids.CamSelectStackLayout.add_widget(box)
def capture_video(self, url, widget):
capture = cv2.VideoCapture(url, cv2.CAP_FFMPEG, [cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_ANY])
print(url)
while True:
# read the video frame within the thread
ret, frame = capture.read()
if ret:
# update the widget texture on the main thread
Clock.schedule_once(lambda dt: self.update_texture(frame, widget))
break
else:
# release the capture and stop the thread if there’s an error
capture.release()
break
def update_texture(self, frame, widget):
buf = cv2.flip(frame, 0).tobytes()
texture = Texture.create(size=(frame.shape[1], frame.shape[0]), colorfmt="bgr")
texture.blit_buffer(buf, colorfmt="bgr", bufferfmt="ubyte")
widget.texture = texture
class StreamView(Screen):
output = StringProperty()
#event = None
def on_enter(self):
app = App.get_running_app()
self.output = app.data
print("before calling cv capture")
self.capture = cv2.VideoCapture(self.output, cv2.CAP_FFMPEG, [cv2.CAP_PROP_HW_ACCELERATION, cv2.VIDEO_ACCELERATION_ANY])
print("after calling cv capture")
#b = Button(size_hint = (0.05, 0.05), pos_hint = {"top":1}, background_color = (1,0,0,1))
b = ButtonImage(size_hint = (0.05, 1), pos_hint = {"top":1},source = "BackButton.png")
b.bind(on_press=lambda instance, button_id="i": gotomainscreen(button_id))
self.Box = BoxLayout()
self.image = Image()
self.add_widget(self.Box)
print("before starting clock")
self.event = Clock.schedule_interval(self.update, 1.0 / 30.0)
self.Box.add_widget(b)
self.Box.add_widget(self.image)
def gotomainscreen(x):
app = App.get_running_app()
app.root.current = "Main"
def update(self, dt):
ret, frame = self.capture.read()
if ret:
buf = cv2.flip(frame, 0).tobytes()
texture = Texture.create(size=(frame.shape[1], frame.shape[0]), colorfmt='bgr')
texture.blit_buffer(buf, colorfmt='bgr', bufferfmt='ubyte')
self.image.texture = texture
def on_leave(self):
self.capture.release()
self.event.cancel()
self.Box.clear_widgets()
cv2.destroyAllWindows()
class ScreenMan(ScreenManager):
pass
class IPCAMApp(App):
def build(self):
sm = ScreenMan()
sm.add_widget(MainScreen(name="Main"))
sm.add_widget(StreamView(name="Stream"))
sm.current = "Main"
return sm
if __name__ == "__main__":
IPCAMApp().run()
|
ade168e2af9d450c83cff3c83b795d7d
|
{
"intermediate": 0.30315080285072327,
"beginner": 0.5215139389038086,
"expert": 0.17533524334430695
}
|
8,141
|
I am writing scrapers using residential proxies(quite expensive), which I noticed the traffic was quite heavy and normally Selenium sends more than one request for a single URL. What can i do to reduce traffic using selenium in python?
|
f1f55dacedc15eadf74c144c85164ea1
|
{
"intermediate": 0.5847978591918945,
"beginner": 0.17245693504810333,
"expert": 0.24274517595767975
}
|
8,142
|
create a very simple javafx network based game
|
2a180bef54f919faa69b2ed07ce4a67c
|
{
"intermediate": 0.24600055813789368,
"beginner": 0.16536369919776917,
"expert": 0.5886357426643372
}
|
8,143
|
why std::atomic needs memory order when load/store.
|
3d88f55b0c58d25f59079f9e4a059a15
|
{
"intermediate": 0.4830353260040283,
"beginner": 0.2153395414352417,
"expert": 0.30162513256073
}
|
8,144
|
write a python function that move the rest .zip files into ./ unzip folder
|
1570d55746ca7a56247e7d91e5ca8dc5
|
{
"intermediate": 0.35911643505096436,
"beginner": 0.2727918326854706,
"expert": 0.3680916726589203
}
|
8,145
|
pada kode analisis sentimen berikut, tambahkan model CNN-LSTM ke kodenya dan atur sedemikian rupa, tambahkan kode lain jika diperlukan!
Kode analisis sentimen:
import pickle
import pandas as pd
import numpy as np
import torch
from torch.utils.data import Dataset
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, accuracy_score
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer
# Membaca dataset
data_list = pickle.load(open('filtered_pre_processed_berita_joined_done.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Nama_Pelabel'])
data['Isi Berita'] = data['pre_processed']
# Tentukan label
sentiment_mapping = {'Positif': 0, 'Negatif': 1, 'Netral': 2}
data['Label'] = data['Label'].map(sentiment_mapping)
# Split dataset
train_texts, test_texts, train_labels, test_labels = train_test_split(data['Isi Berita'].tolist(), data['Label'].tolist(), test_size=0.20, random_state=42)
# Load tokenizer dan model
tokenizer = AutoTokenizer.from_pretrained('indolem/indobert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('indolem/indobert-base-uncased', num_labels=len(sentiment_mapping))
# Atur dataset
class NewsDataset(Dataset):
def __init__(self, texts, labels, tokenizer, max_len):
self.texts = texts
self.labels = labels
self.tokenizer = tokenizer
self.max_len = max_len
def __len__(self):
return len(self.texts)
def __getitem__(self, index):
text = str(self.texts[index])
label = self.labels[index]
inputs = self.tokenizer.encode_plus(
text, add_special_tokens=True, max_length=self.max_len, padding='max_length', truncation=True
)
return {'input_ids': torch.tensor(inputs['input_ids'], dtype=torch.long),
'attention_mask': torch.tensor(inputs['attention_mask'], dtype=torch.long),
'labels': torch.tensor(label, dtype=torch.long)
}
max_len = 400
train_dataset = NewsDataset(train_texts, train_labels, tokenizer, max_len)
test_dataset = NewsDataset(test_texts, test_labels, tokenizer, max_len)"
Kode CNN-LSTM:
" # Membangun model untuk lipatan saat ini
model = Sequential([
Embedding(input_dim=10000,
output_dim=300,
input_length=500,
weights=[embedding_matrix],
trainable=False),
Conv1D(256, 3, activation='relu', kernel_regularizer=l2(0.01)),
GlobalMaxPooling1D(),
Dropout(0.5),
Reshape((-1, 256)),
LSTM(200, return_sequences=False, kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01)),
Dropout(0.7),
Dense(32, activation='relu', kernel_regularizer=l2(0.01)),
Dropout(0.5),
Dense(3, activation='softmax')
])
# Buat callback early stopping
callback_es = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)
initial_learning_rate = 0.0001
optimizer = Adam(learning_rate=initial_learning_rate)
# Kompilasi model
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
# Latih model dengan menggunakan learning rate scheduler dan early stopping dalam callbacks
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=128, callbacks=[callback_es])"
di akhir tambahkan kode akurasi dan classification report:
"# Menguji model
predictions, true_labels, _ = trainer.predict(test_dataset)
predicted_labels = np.argmax(predictions, axis=1)
print(classification_report(test_labels, predicted_labels))
print("Accuracy: ", accuracy_score(test_labels, predicted_labels))"
|
f98535d3b58d5c71a1ccab307adf5bca
|
{
"intermediate": 0.3257251977920532,
"beginner": 0.3468252420425415,
"expert": 0.3274495601654053
}
|
8,146
|
Buat kode python analisis sentimen CNN-LSTM (layer CNN kemudian layer LSTM) menggunakan embedding (vektor representasi) IndoBERT! Kode awal menggunakan ini:
"# Membaca dataset
data_list = pickle.load(open('pre_processed_berita_121_joined_FIX_parpolheuristic_added.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Partai_Politik_Heuristic'])
data['Isi Berita'] = data['pre_processed']
# Tokenisasi dan Padding
max_words = 10000 # mengurangi jumlah kata maksimum
max_len = 500 # mengurangi panjang input
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(data['Isi Berita'])
sequences = tokenizer.texts_to_sequences(data['Isi Berita'])
X = pad_sequences(sequences, maxlen=max_len)
y = to_categorical(data['Label'].astype('category').cat.codes)
# Membagi data menjadi data latih dan data uji
X_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, data.index, test_size=0.2, random_state=42)"
Kode CNN-LSTM:
" Conv1D(256, 3, activation='relu', kernel_regularizer=l2(0.01)),
MaxPooling1D(pool_size=4),
LSTM(200, return_sequences=False, kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01)),
Dense(32, activation='relu', kernel_regularizer=l2(0.01)),
Dropout(0.5),
Dense(3, activation='softmax')"
|
e852f0991f0b9faa06f35a448bf040e2
|
{
"intermediate": 0.2608654797077179,
"beginner": 0.20449504256248474,
"expert": 0.5346395373344421
}
|
8,147
|
Kode analisis sentimen ini:
"import pickle
import pandas as pd
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.utils import pad_sequences
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense, Dropout, Conv1D, MaxPooling1D
from keras.layers import Flatten
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report
# Tambahkan import berikut
from transformers import AutoTokenizer, AutoModel
# Import libraries yang diperlukan
import numpy as np
from keras.models import Sequential
from keras.layers import Input, Embedding, Conv1D, MaxPooling1D, LSTM, Dense, Dropout
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.regularizers import l2
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import accuracy_score, classification_report
from transformers import BertTokenizer, TFBertModel
import tensorflow as tf
# Parameter untuk regularisasi L2
l2_coeff = 0.01
# Membaca dataset
data_list = pickle.load(open('pre_processed_berita_121_joined_FIX_parpolheuristic_added.pkl', 'rb'))
data = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Partai_Politik_Heuristic'])
data['Isi Berita'] = data['pre_processed']
# Tokenisasi dan Padding
max_len = 500 # mengurangi panjang input
tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-large-p1")
sequences = tokenizer(list(data['Isi Berita']), padding='max_length', truncation=True, max_length=max_len, return_tensors="np")
X = sequences['input_ids']
y = to_categorical(data['Label'].astype('category').cat.codes)
# Inisialisasi StratifiedKFold
n_splits = 5
skf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)
# Konversi kategori target ke nilai numerik
y_categorical = data['Label'].astype('category').cat.codes
# Menyimpan akurasi, report, dan history untuk setiap fold
accuracies = []
class_reports = []
histories = []
# Load tokenizer dari IndoBERT
tokenizer = BertTokenizer.from_pretrained("indolem/indobert-base-uncased")
# Fungsi helper untuk meng-encode teks
def encode_sentence(sentence):
encode = tokenizer.encode_plus(
sentence,
max_length=500,
padding="max_length",
truncation=True,
return_attention_mask=True,
return_token_type_ids=False,
return_tensors="tf",
)
return encode["input_ids"], encode["attention_mask"]
# Encode teks dari dataset
sentences = data["Isi Berita"]
sentences_encoded = [encode_sentence(sentence) for sentence in sentences]
X = np.array([np.asarray(encoded) for encoded in sentences_encoded], dtype=object)
iter_num = 1
for train_index, test_index in skf.split(X, y_categorical):
print("\nTraining and evaluating model for fold", iter_num)
# Membagi data berdasarkan indeks dari fold saat ini
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
# Ambil model IndoBERT yang sudah dilakukan fine-tuning
indobert_layer = TFBertModel.from_pretrained("indolem/indobert-base-uncased")
# Input dan attention mask dari IndoBERT
input_ids = Input(shape=(500,), dtype=tf.int32)
attention_mask = Input(shape=(500,), dtype=tf.int32)
# Embedding dari model IndoBERT
embedding = indobert_layer(input_ids, attention_mask=attention_mask)[0]
# Membangun model untuk fold saat ini
x = Conv1D(256, 3, activation='relu', kernel_regularizer=l2(0.01))(embedding)
x = MaxPooling1D(pool_size=4)(x)
x = LSTM(200, return_sequences=False, kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01))(x)
x = Dense(32, activation='relu', kernel_regularizer=l2(0.01))(x)
x = Dropout(0.5)(x)
output = Dense(3, activation='softmax')(x)
model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=output)
# Buat callback early stopping
callback_es = EarlyStopping(monitor='val_loss', patience=5, verbose=1,
restore_best_weights=True)
# Menyusun Model
optimizer = Adam(learning_rate=0.0001)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
# Melatih Model
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=100, batch_size=128,
callbacks=[callback_es])
histories.append(history)
model.save('CNN-LSTM_IndoBERT_CNN256_LSTM200_' + str(iter_num) + '.h5')
# Evaluasi model pada lipatan saat ini
y_pred = model.predict(X_test)
y_pred_labels = np.argmax(y_pred, axis=1)
y_test_labels = np.argmax(y_test, axis=1)
# Menghitung akurasi
accuracy = accuracy_score(y_test_labels, y_pred_labels)
print("Fold", iter_num, "Accuracy: {:.2f}%".format(accuracy * 100))
# Classification report
class_report = classification_report(y_test_labels, y_pred_labels)
# Menambahkan akurasi dan report ke daftar
accuracies.append(accuracy)
class_reports.append(class_report)
iter_num += 1
# Hitung rata-rata akurasi dan cetak report klasifikasi untuk setiap fold
average_accuracy = np.mean(accuracies)
print("\nAverage accuracy across folds: {:.2f}%".format(average_accuracy * 100))
for i, report in enumerate(class_reports):
print("\nClassification report for fold", i + 1)
print(report)"
menghasilkan error berikut, bagaimana solusinya?
"OSError Traceback (most recent call last)
Cell In[10], line 9
6 y_train, y_test = y[train_index], y[test_index]
8 # Ambil model IndoBERT yang sudah dilakukan fine-tuning
----> 9 indobert_layer = TFBertModel.from_pretrained("indolem/indobert-base-uncased")
11 # Input dan attention mask dari IndoBERT
12 input_ids = Input(shape=(500,), dtype=tf.int32)
File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\transformers\modeling_tf_utils.py:2677, in TFPreTrainedModel.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs)
2671 has_file_kwargs = {
2672 "revision": revision,
2673 "proxies": proxies,
2674 "use_auth_token": use_auth_token,
2675 }
2676 if has_file(pretrained_model_name_or_path, WEIGHTS_NAME, **has_file_kwargs):
-> 2677 raise EnvironmentError(
2678 f"{pretrained_model_name_or_path} does not appear to have a file named"
2679 f" {TF2_WEIGHTS_NAME} but there is a file for PyTorch weights. Use `from_pt=True` to"
2680 " load this model from those weights."
2681 )
2682 else:
2683 raise EnvironmentError(
2684 f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME},"
2685 f" {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
2686 )
OSError: indolem/indobert-base-uncased does not appear to have a file named tf_model.h5 but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those weights."
|
1e60fa1a5395df9701621d527f8a4346
|
{
"intermediate": 0.41378846764564514,
"beginner": 0.3380308151245117,
"expert": 0.24818071722984314
}
|
8,148
|
js如何将blob数据转换为canvas
|
9b27f55e870273ece14fb5d9b7d231f4
|
{
"intermediate": 0.42121824622154236,
"beginner": 0.2932678759098053,
"expert": 0.28551387786865234
}
|
8,149
|
Complete Example of Using DMA to Copy Data in the Linux 4.19 Kernel
|
bca4bd08839b147c863ad66da7ad5469
|
{
"intermediate": 0.373007208108902,
"beginner": 0.20727814733982086,
"expert": 0.4197145700454712
}
|
8,150
|
Set up your development environment with the necessary tools and frameworks, including React and Django.
Create a new React project and install the necessary dependencies, including Tailwind CSS.
Build the user interface of the application using React components and Tailwind CSS classes. Include a search bar where users can enter a location, and display the weather conditions, temperature, and humidity for that location.
Create a Django project and set up a new app to handle the weather API requests.
Use the requests library to make HTTP requests to the OpenWeatherMap API and retrieve the weather data for the specified location.
Create a Django view to handle the API requests and return the weather data as a JSON response.
Connect the front-end and back-end of the application using HTTP requests. Use the Axios library to make HTTP requests from the React components to the Django API.
Test the application thoroughly to ensure that it works as expected. Use tools like Jest and Enzyme to test the React components, and use Django's built-in testing framework to test the API endpoints.
Add funky styles and animations to make the application more visually appealing and engaging for users. Use Tailwind CSS classes and CSS animations to add some flair to the user interface.
Remember to follow best practices for web development, including writing clean and maintainable code, using version control, and testing thoroughly. Good luck with your project!
|
5b8096ff27c04a8a58da3c01e1056fc6
|
{
"intermediate": 0.7049263119697571,
"beginner": 0.14624568819999695,
"expert": 0.14882799983024597
}
|
8,151
|
fastlane SPACESHIP_SKIP_2FA_UPGRADE
|
7be09eec597b2088453d191ecbc943e0
|
{
"intermediate": 0.3686336874961853,
"beginner": 0.21046215295791626,
"expert": 0.42090415954589844
}
|
8,152
|
<View
className={item.column === props.tabActive ? `${styles['more-active']} ${styles['more-tab-item']}` : styles['more-tab-item']}
data-type={item.column}
data-name={item.name}
data-tabName={item.columnName}
onClick={checkMoreTime}
>{item.name} 要怎么让styles['more-active']}覆盖${styles['more-tab-item']}中重复的属性
|
d1098b763b68ab0c2e753cb7baa7e09e
|
{
"intermediate": 0.41685038805007935,
"beginner": 0.39023256301879883,
"expert": 0.192917138338089
}
|
8,153
|
Linux 4.19 kernel using platform_ device_ Alloc registers DMA devices and starts DMA copying data
|
8fe09a525a64fd7619392f0e96d6d9e1
|
{
"intermediate": 0.3967564105987549,
"beginner": 0.23128920793533325,
"expert": 0.37195444107055664
}
|
8,154
|
use html,css,javascript,clone Riot Client Landing Page
|
2d3b57917f0884ee8a651c9fafc86ab5
|
{
"intermediate": 0.40420469641685486,
"beginner": 0.3435536026954651,
"expert": 0.2522416412830353
}
|
8,155
|
data
Set up your development environment with the necessary tools and frameworks, including React and Django.
Create a new React project and install the necessary dependencies, including Tailwind CSS.
Build the user interface of the application using React components and Tailwind CSS classes. Include a search bar where users can enter a location, and display the weather conditions, temperature, and humidity for that location.
Create a Django project and set up a new app to handle the weather API requests.
Use the requests library to make HTTP requests to the OpenWeatherMap API and retrieve the weather data for the specified location.
Create a Django view to handle the API requests and return the weather data as a JSON response.
Connect the front-end and back-end of the application using HTTP requests. Use the Axios library to make HTTP requests from the React components to the Django API.
Test the application thoroughly to ensure that it works as expected. Use tools like Jest and Enzyme to test the React components, and use Django’s built-in testing framework to test the API endpoints.
Add funky styles and animations to make the application more visually appealing and engaging for users. Use Tailwind CSS classes and CSS animations to add some flair to the user interface.
Remember to follow best practices for web development, including writing clean and maintainable code, using version control, and testing thoroughly. Good luck with your project!
As an AI language model, I cannot create a development environment and code. However, I can give you the list of steps that can be followed to create a weather application using React and Django.
give the detailed list of steps
Sure, here is a detailed list of steps you can follow to create a weather application using React and Django:
1. Install necessary tools and frameworks: You will need to install the necessary tools and frameworks for creating web applications with React and Django. This includes installing Node.js, npm (Node Package Manager), and Django on your local machine.
2. Create a new React project: Use the create-react-app CLI tool to quickly create a new React project. Run the following command in your terminal:
npx create-react-app my-app
3. Install necessary dependencies: Once your React project is created, you will need to install necessary dependencies, including Tailwind CSS and Axios. Run the following commands in your terminal:
npm install tailwindcss axios
4. Set up Tailwind CSS: You will need to configure Tailwind CSS in your React project. Create a new file called tailwind.config.js in the root directory of your project, and add the following code:
module.exports = {
purge: [],
darkMode: false, // or ‘media’ or ‘class’
theme: {
extend: {},
},
variants: {
extend: {},
},
plugins: [],
}
Create a new file called index.css in the src directory of your project, and add the following code:
@import ‘tailwindcss/base’;
@import ‘tailwindcss/components’;
@import ‘tailwindcss/utilities’;
Update index.js to import index.css:
import React from ‘react’;
import ReactDOM from ‘react-dom’;
import ‘./index.css’;
import App from ‘./App’;
import reportWebVitals from ‘./reportWebVitals’;
ReactDOM.render(
<React.StrictMode>
<App />
</React.StrictMode>,
document.getElementById(‘root’)
);
reportWebVitals();
5. Build the user interface: Use React components and Tailwind CSS classes to build the user interface of your weather application. Create a search bar where users can enter a location, and display the weather conditions, temperature, and humidity for that location.
6. Create a Django project: Use the django-admin CLI tool to create a new Django project. Run the following command in your terminal:
django-admin startproject myproject
7. Create a new app: Use the manage.py CLI tool to create a new Django app that will handle the weather API requests. Run the following command in your terminal:
python manage.py startapp weatherapp
8. Make HTTP requests to the OpenWeatherMap API: Use the requests library to make HTTP requests to the OpenWeatherMap API and retrieve the weather data for the specified location. Create a new file called weather.py in the weatherapp directory of your project, and add the following code:
import requests
import json
def get_weather_data(location):
url = f’http://api.openweathermap.org/data/2.5/weather?q={location}&appid=<your-api-key>&units=metric’
response = requests.get(url)
if response.status_code == 200:
return response.json()
else:
return None
Replace <your-api-key> with your actual OpenWeatherMap API key.
9. Create a Django view: Create a Django view that will handle the API requests and return the weather data as a JSON response. Update views.py in the weatherapp directory, and add the following code:
from django.http import JsonResponse
from .weather import get_weather_data
def weather(request):
location = request.GET.get(‘location’)
data = get_weather_data(location)
if data is not None:
return JsonResponse(data)
else:
return JsonResponse({‘error’: ‘Location not found’})
10. Set up Django URLs: Set up the URLs for your Django project and app. Update urls.py in the myproject directory, and add the following code:
from django.contrib import admin
from django.urls import path, include
urlpatterns = [
path(‘admin/’, admin.site.urls),
path(‘weather/’, include(‘weatherapp.urls’)),
]
Create a new file called urls.py in the weatherapp directory, and add the following code:
from django.urls import path
from . import views
urlpatterns = [
path(‘’, views.weather, name=‘weather’),
]
11. Connect the front-end and back-end: Use Axios to make HTTP requests from the React components to the Django API. Update your React components to make API requests to the Django API. For example, you can create a new file called weather.js in the src directory, and add the following code:
import axios from ‘axios’;
const API_URL = ‘http://localhost:8000/weather/’;
export const getWeatherData = async (location) => {
try {
const response = await axios.get(API_URL, {
params: {
location: location,
},
});
return response.data;
} catch (error) {
throw error;
}
};
12. Test the application: Test your application thoroughly to ensure that it works as expected. Use tools like Jest and Enzyme to test the React components, and use Django’s built-in testing framework to test the API endpoints.
13. Add styles and animations: Add funky styles and animations to make the application more visually appealing and engaging for users. Use Tailwind CSS classes and CSS animations to add some flair to the user interface.
how should be the folder structure for above project
Here is an example of a folder structure that you can use for your React and Django project:
my-project
├── myproject
│ ├── init.py
│ ├── asgi.py
│ ├── settings.py
│ ├── urls.py
│ └── wsgi.py
└── weatherapp
├── init.py
├── admin.py
├── apps.py
├── models.py
├── tests.py
├── urls.py
├── views.py
└── weather.py
├── myproject
│ ├── init.py
│ ├── asgi.py
│ ├── settings.py
│ ├── urls.py
│ ├── wsgi.py
│ └── templates
│ └── index.html
└── src
├── App.js
├── App.test.js
├── index.css
├── index.js
├── reportWebVitals.js
├── setupTests.js
├── weather.js
├── components
│ ├── SearchBar.js
│ └── WeatherInfo.js
└── styles
├── tailwind.config.js
├── index.css
├── index.css.d.ts
├── components
│ ├── SearchBar.css
│ └── WeatherInfo.css
└── styles.css
The my-project directory is the root directory of your project. myproject is the Django project directory, and weatherapp is the Django app directory that handles the API requests.
The templates directory in the myproject directory contains a basic HTML file, where the React app will be rendered.
The src directory contains JavaScript files for your React components, as well as a styles directory for Tailwind CSS configuration and component-specific styles.
By keeping your React app within Django, you can make use of Django’s built-in templating engine and easily render your React app within a single HTML file.
5. Build the user interface: Use React components and Tailwind CSS classes to build the user interface of your weather application. Create a search bar where users can enter a location, and display the weather conditions, temperature, and humidity for that location.
how to build the above with funky and beautiful ui
To build a funky and beautiful UI for the weather application, you can make use of various Tailwind CSS classes and React components. Here are some steps you can follow:
1. Create a SearchBar component: Use the useState and useEffect hooks to manage the state of the search bar input value and the weather data retrieved from the API. You can use the w-full and text-center classes to make the search bar occupy the full width of its container and center its text.
2. Create a WeatherInfo component: Use the weather data retrieved from the API to display the weather conditions, temperature, and humidity for the location entered in the search bar. You can use different Tailwind CSS classes to make the temperature and humidity text stand out and be more visible on the page.
3. Add some background images and animations: You can use CSS animations and the bg-cover and bg-center classes to add some motion and life to your UI. For example, you can add a background image of clouds moving across the sky, or of the sun rising and setting behind a city skyline.
4. Make use of Tailwind CSS utility classes: Tailwind CSS has a wide range of utility classes that can help you style your UI more easily and efficiently. For example, you can use the rotate-3d and scale-90 classes to add some 3D transforms and scaling to your UI elements.
5. Use color schemes and gradients: You can use various color schemes and gradients to make your UI more visually appealing and engaging for users. You can create your own custom color scheme, or make use of various online tools and libraries that provide a wide range of color palettes and gradients.
Overall, the key to building a funky and beautiful UI for your weather application is to be creative, experiment with different styles and components, and make use of any tools and resources available to you, like Tailwind CSS and various online design tools.
give example code for above
Certainly! Here are some examples of code for building a funky and beautiful UI for a weather application using React and Tailwind CSS:
SearchBar.js
import React, { useState, useEffect } from ‘react’;
import { getWeatherData } from ‘…/weather’;
const SearchBar = () => {
const [inputValue, setInputValue] = useState(‘’);
const [weatherData, setWeatherData] = useState(null);
useEffect(() => {
if (inputValue !== ‘’) {
getWeatherData(inputValue)
.then((data) => setWeatherData(data))
.catch((error) => console.error(error));
}
}, [inputValue]);
const handleInputChange = (event) => {
setInputValue(event.target.value);
};
return (
<div className=“bg-white rounded-lg py-4 px-8 shadow-lg”>
<input
type=“text”
value={inputValue}
onChange={handleInputChange}
placeholder=“Enter location”
className=“w-full text-center text-2xl bg-transparent border-b-2 border-gray-400 focus:border-indigo-500 focus:outline-none”
/>
{weatherData !== null && (
<div className=“mt-4 flex flex-col items-center”>
<h2 className=“text-2xl font-bold text-indigo-500”>
{weatherData.name}
</h2>
<p className=“text-lg font-bold”>
{weatherData.main.temp.toFixed(1)}°C
</p>
<p className=“text-lg”>
{weatherData.weather[0].description.toUpperCase()}
</p>
<p className=“text-lg”>Humidity: {weatherData.main.humidity}%</p>
</div>
)}
</div>
);
};
export default SearchBar;
This code defines a SearchBar component that displays a search bar where users can enter a location and retrieve the weather data for that location. The useState and useEffect hooks are used to manage the state of the input value and the weather data retrieved from the API. The getWeatherData function is defined in a separate weather.js file and makes use of the Axios library to make HTTP requests to the OpenWeatherMap API. The component uses various Tailwind CSS classes to style the search bar and weather information, including bg-white, rounded-lg, py-4, px-8, shadow-lg, border-b-2, and focus:border-indigo-500.
WeatherInfo.js
import React from ‘react’;
const WeatherInfo = ({ weatherData }) => {
return (
<div
className=“bg-gradient-to-r from-green-400 to-blue-500 rounded-lg py-8 px-12 shadow-lg”
style={{
backgroundImage:
‘url(“https://source.unsplash.com/1600x900/?sky,weather”)’,
backgroundRepeat: ‘no-repeat’,
backgroundPosition: ‘center’,
backgroundSize: ‘cover’,
}}
>
<h2 className=“text-3xl font-bold text-white”>{weatherData.name}</h2>
<p className=“text-xl font-bold text-white”>
{weatherData.main.temp.toFixed(1)}°C
</p>
<p className=“text-md font-bold text-white uppercase”>
{weatherData.weather[0].description}
</p>
<p className=“text-md text-white”>Humidity: {weatherData.main.humidity}%</p>
</div>
);
};
export default WeatherInfo;
This code defines a WeatherInfo component that displays the weather information for the location entered in the search bar. The component receives the weatherData object as a prop, which contains the weather conditions, temperature, and humidity for the location. The component uses various Tailwind CSS utility classes to style the background gradient, and uses inline styles to set the background image and size.
These are just examples of what your code could look like - they may need to be customized and modified to better suit your specific project’s needs and design priorities.
give code for App.js
Sure, here’s an example of an App.js file that renders the SearchBar and WeatherInfo components, and styles the overall layout of the weather application using Tailwind CSS:
import React from ‘react’;
import SearchBar from ‘./components/SearchBar’;
import WeatherInfo from ‘./components/WeatherInfo’;
import ‘./styles/index.css’;
const App = () => {
return (
<div
className=“bg-gray-100 min-h-screen flex flex-col items-center justify-center”
style={{
backgroundImage: ‘url(“https://source.unsplash.com/1600x900/?clouds”)’,
backgroundRepeat: ‘no-repeat’,
backgroundPosition: ‘center’,
backgroundSize: ‘cover’,
}}
>
<div className=“mt-16”>
<SearchBar />
</div>
<div className=“mt-16”>
<WeatherInfo />
</div>
</div>
);
};
export default App;
This code defines an App component that uses Tailwind CSS utility classes to style the overall layout of the weather application. The component renders the SearchBar and WeatherInfo components, which are defined in separate files within the src directory. The component sets the background image of the application using inline styles, and uses the min-h-screen, flex, items-center, and justify-center classes to center the content on the page.
code for index.js
Sure, here is some example code for index.js file that renders the App component to the div element with the id of “root” in the HTML template:
import React from ‘react’;
import ReactDOM from ‘react-dom’;
import App from ‘./App’;
ReactDOM.render(
<React.StrictMode>
<App />
</React.StrictMode>,
document.getElementById(‘root’)
);
This code imports the ReactDOM library and the App component, and uses the ReactDOM.render() method to render the App component to the HTML element with the id of “root”. The StrictMode component is used to enable additional runtime checks for common React development issues.
code for reportWebVitals.js
Here’s an example of the reportWebVitals.js file:
const reportWebVitals = (onPerfEntry) => {
if (onPerfEntry && onPerfEntry instanceof Function) {
import(‘web-vitals’).then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => {
getCLS(onPerfEntry);
getFID(onPerfEntry);
getFCP(onPerfEntry);
getLCP(onPerfEntry);
getTTFB(onPerfEntry);
});
}
};
export default reportWebVitals;
This code defines a reportWebVitals function that accepts a callback function (onPerfEntry) as an argument. It checks if onPerfEntry is a function and if the web-vitals library is installed. If both conditions are true, it imports the getCLS, getFID, getFCP, getLCP, and getTTFB functions from the web-vitals library, and calls them with onPerfEntry as the argument.
prompt-from above give example for weather.js
|
363d8158238a6474cd4c8896cb90a4c1
|
{
"intermediate": 0.5651031732559204,
"beginner": 0.2751641273498535,
"expert": 0.15973275899887085
}
|
8,156
|
if I wanted to build my own custom ChatGPT that has voice to voice capability what would my core or base code look like?
|
29da6bcf0be6c5e6cff4edb3fb85ac1a
|
{
"intermediate": 0.3224737048149109,
"beginner": 0.2088923454284668,
"expert": 0.4686339497566223
}
|
8,157
|
stop_details=(f'{order.getordername()} Price: {order.executed.price}, size: {round(abs(order.size),2)}')如何获取日期
|
d301fe5621b914ec05106acc1bee081a
|
{
"intermediate": 0.33310362696647644,
"beginner": 0.2990531325340271,
"expert": 0.3678432106971741
}
|
8,158
|
定义皇冠梨订购成本为 1.5元，售价 2.1 元，过期成本 1.7 元,缺货成本 0.4 元，持有成本 0.1 元，提前期为 1 天，生命周期为 3 天，对动作数量和数值进行调整，对 Q 学习算法模型进行训练。以生鲜农产品零售经营者利润为奖励函数，在实验过程中，生鲜农产品零售商利润以天为单位，进行 500 天实验，从而得出生鲜产品零售商的总利润和库存成本。零售经营者采用服务水平为96%的定量订货模型，利润以天为单位，进行 500 天的计算，得出的总利润和库存成本。总利润=500*R，库存成本为 U。实验以 E=0.1，学习率初始化为 0.7，当训练 2 万次，每迭代千次便将学习率降低为𝛼 ∗ 𝛽，𝛽 = 0.9。实验的每个周期为 500 次，每个实验周期只进行一次订货与库存更新。在实验条件均相同的情况下，当需求数据是𝐿 = (60，5^2)时，求出零售商使用基于 Q 学习算法和与定量订货法的库存模型得到的的库存成本，画出折线图对比，并列出需求满足𝐿 = (60，5^2)库存成本数据（包含日期,需求,存量,过期量,缺货,订货动作,库存变动,缺货成本，过期成本，持有成本，利润），展现Q 表更新结果图。状态变量：本文将状态变量设为𝑆,𝑂𝑂：表示批发商从零售商收到的订单的大小，𝐴𝑂：表示零售商从批发商收到的货物的多少，𝑋：表示零售商的库存水平，𝐼：表示零售商从顾客了解到的需求。𝑆 = [𝑂𝑂,𝐴𝑂, 𝑋,𝐼]，动作变量：本文将智能体动作变量设为 A， 即选择订货的数量。根据实际情况，将动作函数连续离散化，A 取整数，𝐴 ∈ [0,𝐾]。𝐴𝑡 = [𝐾𝑡]。零售商每日的利润，综合作为本模型的奖惩函数 R。𝑅 = 𝑤𝐼 − (𝐶 ∙ 𝐶𝑛 + 𝑃𝑛 ∙ 𝑃 + 𝐺𝑛 ∙ 𝐺)。其中，w：商品单个盈利，I：客户需求，C：单位产品的过期成本，𝐶𝑛：每段时间内的过期数量，𝑃 ：单位产品的持有成本，𝑃𝑛 ：每段时间内持有的商品的数量，𝐺 ：单位产品的缺货成本，𝐺𝑛 ：每段时间内的缺货数量。麻烦给出完整的代码过程。要求用python3.0版本无报错运行
|
179b04defd3f2d367dcc139b38bdfe02
|
{
"intermediate": 0.2239941954612732,
"beginner": 0.5949217081069946,
"expert": 0.18108411133289337
}
|
8,159
|
use "int" in c# as another name?
|
94a1b53cdd3ae6cafadbccfd43d18f0e
|
{
"intermediate": 0.4157048761844635,
"beginner": 0.33121365308761597,
"expert": 0.2530815303325653
}
|
8,160
|
I have a table in python with GPS coordinates saved as latitudes and longitudes and cluster labels. I need to find the max and min GPS coordinates per cluster label. How can I loop the code based on the number of cluster labels
|
336388a2abc26fa10d4b29c2368f3b48
|
{
"intermediate": 0.4235897958278656,
"beginner": 0.2788727283477783,
"expert": 0.29753750562667847
}
|
8,161
|
transition: background-color 5s, color 5s infinite alternate-reverse;
|
1b734eec1000ccb4d030a08e0aae9b80
|
{
"intermediate": 0.42758074402809143,
"beginner": 0.21247705817222595,
"expert": 0.3599422574043274
}
|
8,162
|
You are expert and mentoring me, I am planning for a system in which there are several display units each falling under one of 5 category, the display units shall show a welcome message when there isn’t any data to show and when there is data to show the display should show, there is also an operator who can add , update and delete data and thus operation on data shall be reflected on the display as well, please explain in detail in terms of web technology
|
fe68c5ca8fc23a3aa6ec66a8d2efa5fe
|
{
"intermediate": 0.6324414014816284,
"beginner": 0.05665231868624687,
"expert": 0.31090623140335083
}
|
8,163
|
HERES MY pine script code: //@version=5
indicator("Previous Week Bias", overlay=true, max_labels_count=1, max_lines_count=1)
var line prevWeekHighLabel = na
var line prevWeekLowLabel = na
var line buySignalLine = na
var line sellSignalLine = na
var line buy00Line = na
var line sell00Line = na
var line buy007Line = na
var line sell007Line = na
var label buy007Label = na
var label sell007Label = na
var float prevMonthHigh = na
var float prevMonthLow = na
var float fiftyPercent = na
var float highestHigh = na
var float lowestLow = na
var bool buyBiasConfirmed = na
var bool sellBiasConfirmed = na
var bool redCandleClosed = na
var bool greenCandleClosed = na
var float buy00Price = na
var float sell00Price = na
prevMonthHigh := request.security(syminfo.tickerid, "M", high[1])
prevMonthLow := request.security(syminfo.tickerid, "M", low[1])
fiftyPercent := (prevMonthHigh + prevMonthLow) / 2
prevWeekCloseHigh = request.security(syminfo.tickerid, "W", high[1])
prevWeekCloseLow = request.security(syminfo.tickerid, "W", low[1])
prevWeekClose = request.security(syminfo.tickerid, "W", close[1])
high4H = request.security(syminfo.tickerid, "240", high)
low4H = request.security(syminfo.tickerid, "240", low)
close4H = request.security(syminfo.tickerid, "240", close)
open4H = request.security(syminfo.tickerid, "240", open)
if ta.change(time("W")) > 0
line.delete(prevWeekHighLabel)
line.delete(prevWeekLowLabel)
line.delete(buySignalLine)
line.delete(sellSignalLine)
line.delete(buy00Line)
line.delete(sell00Line)
line.delete(buy007Line)
line.delete(sell007Line)
label.delete(buy007Label)
label.delete(sell007Label)
if prevWeekClose > fiftyPercent
prevWeekHighLabel := line.new(x1=bar_index[1], y1=prevWeekCloseHigh, x2=bar_index, y2=prevWeekCloseHigh, color=color.blue, width=1, style=line.style_solid)
label.new(x=bar_index, y=prevWeekCloseHigh, text="Buy Bias", color=color.green, style=label.style_text_outline, size=size.small)
buyBiasConfirmed := true
else if prevWeekClose < fiftyPercent
prevWeekLowLabel := line.new(x1=bar_index[1], y1=prevWeekCloseLow, x2=bar_index, y2=prevWeekCloseLow, color=color.blue, width=1, style=line.style_solid)
label.new(x=bar_index, y=prevWeekCloseLow, text="Sell Bias", color=color.red, style=label.style_text_outline, size=size.small)
sellBiasConfirmed := true
// Buy bias logic
if buyBiasConfirmed
if (close4H) > prevWeekCloseHigh
high4H := buy00Price
else if high4H > buy00Price
high4H := buy00Price
else if (close4H) < open4H
redCandleClosed := true
else if redCandleClosed and not na(buy00Price)
buy007Line := line.new(x1=bar_index, y1=buy00Price, x2=bar_index, y2=buy00Price, color=color.rgb(0, 21, 249), width=1, style=line.style_solid)
label.new(x=bar_index, y=buy00Price, text="Buy 00=7", color=color.green, style=label.style_text_outline, size=size.small)
redCandleClosed := na
// Sell bias logic
if sellBiasConfirmed
if (close4H) < prevWeekCloseLow
sell00Price := low4H
else if low4H < sell00Price
sell00Price := low4H
else if (close4H) > open4H
greenCandleClosed := true
else if greenCandleClosed and not na(sell00Price)
sell007Line := line.new(x1=bar_index, y1=sell00Price, x2=bar_index , y2=sell00Price, color=color.rgb(10, 36, 205), width=1, style=line.style_solid)
label.new(x=bar_index, y=sell00Price, text="Sell 00=7", color=color.red, style=label.style_text_outline, size=size.small)
greenCandleClosed := na
if ta.change(time("W")) > 0
line.delete(prevWeekHighLabel)
line.delete(prevWeekLowLabel)
line.delete(buySignalLine)
line.delete(sellSignalLine)
line.delete(buy00Line)
line.delete(sell00Line)
line.delete(buy007Line)
line.delete(sell007Line)
label.delete(buy007Label)
label.delete(sell007Label)
can you help me stop it from making new lines and labels every 4h candle. i only want each time to occur once per week (but still to be drawn on 4h timeframe)(if at all in case of 00, 00=7). can you also make it so the lines drawn extend 5 candes out from the current candle regardless of what time frame. make sure no duplicate line/labels print per week
|
6c2c4cb24257d692df1046d856b8e329
|
{
"intermediate": 0.2682305574417114,
"beginner": 0.3876039683818817,
"expert": 0.34416553378105164
}
|
8,164
|
HERES MY pine script code: //@version=5
indicator("Previous Week Bias", overlay=true, max_labels_count=1, max_lines_count=1)
var line prevWeekHighLabel = na
var line prevWeekLowLabel = na
var line buySignalLine = na
var line sellSignalLine = na
var line buy00Line = na
var line sell00Line = na
var line buy007Line = na
var line sell007Line = na
var label buy007Label = na
var label sell007Label = na
var float prevMonthHigh = na
var float prevMonthLow = na
var float fiftyPercent = na
var float highestHigh = na
var float lowestLow = na
var bool buyBiasConfirmed = na
var bool sellBiasConfirmed = na
var bool redCandleClosed = na
var bool greenCandleClosed = na
var float buy00Price = na
var float sell00Price = na
prevMonthHigh := request.security(syminfo.tickerid, "M", high[1])
prevMonthLow := request.security(syminfo.tickerid, "M", low[1])
fiftyPercent := (prevMonthHigh + prevMonthLow) / 2
prevWeekCloseHigh = request.security(syminfo.tickerid, "W", high[1])
prevWeekCloseLow = request.security(syminfo.tickerid, "W", low[1])
prevWeekClose = request.security(syminfo.tickerid, "W", close[1])
high4H = request.security(syminfo.tickerid, "240", high)
low4H = request.security(syminfo.tickerid, "240", low)
close4H = request.security(syminfo.tickerid, "240", close)
open4H = request.security(syminfo.tickerid, "240", open)
if ta.change(time("W")) > 0
line.delete(prevWeekHighLabel)
line.delete(prevWeekLowLabel)
line.delete(buySignalLine)
line.delete(sellSignalLine)
line.delete(buy00Line)
line.delete(sell00Line)
line.delete(buy007Line)
line.delete(sell007Line)
label.delete(buy007Label)
label.delete(sell007Label)
if prevWeekClose > fiftyPercent
prevWeekHighLabel := line.new(x1=bar_index[1], y1=prevWeekCloseHigh, x2=bar_index, y2=prevWeekCloseHigh, color=color.blue, width=1, style=line.style_solid)
label.new(x=bar_index, y=prevWeekCloseHigh, text="Buy Bias", color=color.green, style=label.style_text_outline, size=size.small)
buyBiasConfirmed := true
else if prevWeekClose < fiftyPercent
prevWeekLowLabel := line.new(x1=bar_index[1], y1=prevWeekCloseLow, x2=bar_index, y2=prevWeekCloseLow, color=color.blue, width=1, style=line.style_solid)
label.new(x=bar_index, y=prevWeekCloseLow, text="Sell Bias", color=color.red, style=label.style_text_outline, size=size.small)
sellBiasConfirmed := true
// Buy bias logic
if buyBiasConfirmed
if (close4H) > prevWeekCloseHigh
high4H := buy00Price
else if high4H > buy00Price
high4H := buy00Price
else if (close4H) < open4H
redCandleClosed := true
else if redCandleClosed and not na(buy00Price)
buy007Line := line.new(x1=bar_index, y1=buy00Price, x2=bar_index, y2=buy00Price, color=color.rgb(0, 21, 249), width=1, style=line.style_solid)
label.new(x=bar_index, y=buy00Price, text="Buy 00=7", color=color.green, style=label.style_text_outline, size=size.small)
redCandleClosed := na
// Sell bias logic
if sellBiasConfirmed
if (close4H) < prevWeekCloseLow
sell00Price := low4H
else if low4H < sell00Price
sell00Price := low4H
else if (close4H) > open4H
greenCandleClosed := true
else if greenCandleClosed and not na(sell00Price)
sell007Line := line.new(x1=bar_index, y1=sell00Price, x2=bar_index , y2=sell00Price, color=color.rgb(10, 36, 205), width=1, style=line.style_solid)
label.new(x=bar_index, y=sell00Price, text="Sell 00=7", color=color.red, style=label.style_text_outline, size=size.small)
greenCandleClosed := na
if ta.change(time("W")) > 0
line.delete(prevWeekHighLabel)
line.delete(prevWeekLowLabel)
line.delete(buySignalLine)
line.delete(sellSignalLine)
line.delete(buy00Line)
line.delete(sell00Line)
line.delete(buy007Line)
line.delete(sell007Line)
label.delete(buy007Label)
label.delete(sell007Label)
can you help me stop it from making new lines and labels every 4h candle. i only want each time to occur once per week (if at all in case of 00, 00=7). can you also make it so the lines drawn extend 5 candes out from the current candle regardless of what time frame. make sure no duplicate line/labels print per week
|
f88f48910e893b3e0b79cdffa6122d3a
|
{
"intermediate": 0.2682305574417114,
"beginner": 0.3876039683818817,
"expert": 0.34416553378105164
}
|
8,165
|
correct that anim properrely: body,
html {
scrollbar-width: 2em;
scrollbar-color: #fada38 #000000;
overflow-x: hidden;
color:red;
margin: 0;
padding: 0;
height: 100%;
width: 100%;
position: absolute;
--line-height: 1.6;
transition: background-color 5s, color 5s infinite alternate-reverse;
--overflow:hidden;
background-color:rgba(222,222,222,0.5);
}
|
9070b0a981fe6a25f21fb95c3588eb42
|
{
"intermediate": 0.3665684163570404,
"beginner": 0.3151356279850006,
"expert": 0.31829601526260376
}
|
8,166
|
write a program in c++ to reverse a number
|
8bd5fc303f2dedfbccbd08b8600c9e0b
|
{
"intermediate": 0.25865140557289124,
"beginner": 0.25478196144104004,
"expert": 0.4865666329860687
}
|
8,167
|
make me a pine script v5 indicator in proper format and correct syntax. heres what i want the indicator to do: at the beginning of every week take the high and low and close of previous week. take high and low and 50% of the high and low of previous month (i will refer to this as previous month 50%) as well. based on if previous week closed above previous month 50%; call it a buy bias and put a short blue line on previous week high with the label buy bias. if below previous month 50%, then give it a blue line on previous week low with the label sell bias. now look on 4h timeframe; if buy bias, if during this week, there is a 4h close > previous week high then set a flag at the high of this 4h candle. if sell bias, if during this week there is a 4h close <previous week low then set a flat at the low of this 4h candle. then if both conditions have been met, from 1 flag to the next during a buy bias week find the highest 4h candle and on that high make a blue line labelled 007 buy. delete buy bias blue line when this occurs. or from the the first flag to the second, during a sell bias week find the lowest 4h candle and on that low make a blue line labelled 007 sell. delete sell bias blue line when this occurs. make sure all lines are only drawn 1 time during the week, and deleted at end of week so next week can have new lines and labels.
|
62c58c204f323543629b5ebb81722d75
|
{
"intermediate": 0.24789954721927643,
"beginner": 0.4888325333595276,
"expert": 0.2632679045200348
}
|
8,168
|
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import seaborn as sns; sns.set()
import decimal
import csv
import os
os.environ["OMP_NUM_THREADS"] = "1" # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = "4" # export OPENBLAS_NUM_THREADS=4
os.environ["MKL_NUM_THREADS"] = "6" # export MKL_NUM_THREADS=6
os.environ["VECLIB_MAXIMUM_THREADS"] = "4" # export VECLIB_MAXIMUM_THREADS=4
os.environ["NUMEXPR_NUM_THREADS"] = "6" # export NUMEXPR_NUM_THREADS=6
# Variable with the Longitude and Latitude
df=pd.read_csv('Cluster_Input.csv')
X=df.loc[:,['Avg','latitude','longitude','ID']]
""" #Create Clusters and Visualize - Unweighted
#Elbow Method
K_clusters = range(1,10)
kmeans = [KMeans(n_clusters=i) for i in K_clusters]
Y_axis = df[['latitude']]
X_axis = df[['longitude']]
score = [kmeans[i].fit(Y_axis).score(Y_axis) for i in range(len(kmeans))]
# Visualize elbow plot
plt.plot(K_clusters, score)
plt.xlabel('Number of Clusters')
plt.ylabel('Score')
plt.title('Elbow Curve')
plt.show()
#Input optimal number of clusters and print
from tkinter import simpledialog
Optimal_num_clusters = simpledialog.askstring("Optimal #", "Enter optimal # of Clusters:")
Optimal_num_clusters = int(Optimal_num_clusters)
print(Optimal_num_clusters)
# Compute k-means clustering
kmeans = KMeans(n_clusters = Optimal_num_clusters, init ='k-means++')
kmeans.fit(X[X.columns[1:3]])
#Predict Centers
X['cluster_label'] = kmeans.fit_predict(X[X.columns[1:3]])
centers = kmeans.cluster_centers_ # Coordinates of cluster centers.
labels = kmeans.predict(X[X.columns[1:3]]) # Labels of each point
#Visualise Results
X.plot.scatter(x = 'latitude', y = 'longitude', c=labels, s=50, cmap='viridis')
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
plt.title('Clustering GPS Co-ordinates to Form Regions - Unweighted',fontsize=18, fontweight='bold')
plt.show()
print(Optimal_num_clusters) """
##Create Clusters and Visualize, Weighted Using Value from Cluster Input as Weight
#drop nulls
df.dropna(axis=0,how='any',subset=['latitude','longitude','value'],inplace=True)
# Create Variable with the Longitude and Latitude
X_weighted = df.loc[:,['Avg','latitude','longitude','ID']]
#Elbow Method
K_clusters = range(1,10)
kmeans = [KMeans(n_clusters=i) for i in K_clusters]
lat_long = X_weighted[X_weighted.columns[1:2]]
value = X_weighted[X_weighted.columns[0]]
score = [kmeans[i].fit(lat_long, sample_weight = value).score(lat_long) for i in range(len(kmeans))]
# Visualize elbow plot
plt.plot(K_clusters, score)
plt.xlabel('Number of Clusters')
plt.ylabel('Score')
plt.title('Elbow Curve')
plt.show()
#Input optimal number of clusters and print
from tkinter import simpledialog
Optimal_num_clusters_weighted = simpledialog.askstring("Optimal #", "Enter optimal # of Clusters:")
Optimal_num_clusters_weighted = int(Optimal_num_clusters_weighted)
Optimal_num_cluster_weighted_range = range(1,Optimal_num_clusters_weighted)
print(Optimal_num_clusters_weighted)
# Compute weighted k-means clustering
kmeans = KMeans(n_clusters = Optimal_num_clusters_weighted, max_iter=1000, init ='k-means++')
lat_long = X_weighted[X_weighted.columns[1:3]]
value = X_weighted[X_weighted.columns[0]]
weighted_kmeans_clusters = kmeans.fit(lat_long, sample_weight = value) # Compute k-means clustering.
#Predict Centers
X_weighted['cluster_label'] = kmeans.predict(lat_long, sample_weight = value)
centers = kmeans.cluster_centers_ # Coordinates of cluster centers.
labels = X_weighted['cluster_label'] # Labels of each point
#Visualise Results
X_weighted.plot.scatter(x = 'latitude', y = 'longitude', c=labels, s=50, cmap='viridis')
plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5)
plt.title('Optimal Clustering GPS Co-ordinates to Form Regions - Weighted',fontsize=18, fontweight='bold')
plt.show()
print(Optimal_num_clusters_weighted)
#Export to CSV
X_weighted=X_weighted[['ID','cluster_label']]
clustered_data = df.merge(X_weighted, left_on='ID', right_on='ID')
# convert latitude and longitude to decimal degrees
clustered_data['latitude'] = pd.to_numeric(clustered_data['latitude'])
clustered_data['longitude'] = pd.to_numeric(clustered_data['longitude'])
clustered_data.to_csv('clustered_data.csv')
##Find Bounding Boxes
# Get unique cluster labels
cluster_labels = clustered_data['cluster_label'].unique()
# Create a new DataFrame to store the results
results_rows= []
results_table = pd.DataFrame(columns=['cluster_label', 'min_latitude', 'max_latitude', 'min_longitude', 'max_longitude'])
# Loop over cluster labels
for label in cluster_labels:
# Filter table based on the current cluster label
cluster_data_label = clustered_data[clustered_data['cluster_label'] == label]
# Find the minimum and maximum GPS coordinates for the current cluster label
min_latitude = float(np.min(cluster_data_label['latitude']))
max_latitude = np.max(cluster_data_label['latitude'])
min_longitude = np.min(cluster_data_label['longitude'])
max_longitude = np.max(cluster_data_label['longitude'])
# Create a new row with the results and append it to the results table
results_row = {
'cluster_label': [label],
'min_latitude': [min_latitude],
'max_latitude': [max_latitude],
'min_longitude': [min_longitude],
'max_longitude': [max_longitude],
}
results_rows.append(results_row)
results_table = pd.DataFrame(results_rows)
# Print the results table
print(results_table)
#Export bounding boxes to kml
import simplekml
# create a KML file object
kml = simplekml.Kml()
# loop through each row in the results table
for index, row in results_table.iterrows():
# get the minimum and maximum GPS coordinates for the current cluster label
min_latitude = row['min_latitude']
max_latitude = row['max_latitude']
min_longitude = row['min_longitude']
max_longitude = row['max_longitude']
# create a boundary box from the minimum and maximum GPS coordinates
boundary_box = kml.newgroundoverlay(name=f'Cluster {row["cluster_label"]}',
latlonbox=simplekml.LatLonBox(
north=max_latitude,
south=min_latitude,
east=max_longitude,
west=min_longitude
))
# set the image path and transparency of the boundary box
boundary_box.color = simplekml.Color.rgb(255, 255, 255, 127)
# save the KML file
kml.save('boundary_boxes.kml')
|
6b068ff3675a03d6987527ba8a1297a9
|
{
"intermediate": 0.37126779556274414,
"beginner": 0.4532512426376343,
"expert": 0.17548097670078278
}
|
8,169
|
network based tic tac toe javafx game with the simplest way with how to use it
|
4d7444fd1afb08b7ec92631730607fae
|
{
"intermediate": 0.29680749773979187,
"beginner": 0.22440636157989502,
"expert": 0.47878608107566833
}
|
8,170
|
hi how's you
|
9117f61d621145725e6cb9485307c9ad
|
{
"intermediate": 0.35749611258506775,
"beginner": 0.3417370617389679,
"expert": 0.3007667660713196
}
|
8,171
|
why “transition: background-color 15s, color 15s;” doesn’t work infinitely without @keyframes??? but if you want to make some jiggling on hover effect by transition and without any @keyframes???
|
efc07f3811387624a1ddba246d87547d
|
{
"intermediate": 0.5008643269538879,
"beginner": 0.19299481809139252,
"expert": 0.30614086985588074
}
|
8,172
|
without @keyframes??? without jabbaskribtz aka JavaScript???
|
816064e16b7aa938f46307bf875ca253
|
{
"intermediate": 0.5031633377075195,
"beginner": 0.3277755677700043,
"expert": 0.1690610647201538
}
|
8,173
|
(1362 * 450+x * 97)/547=1434.53. Find x
|
51359aae5548a9d9ddb5978f35145b26
|
{
"intermediate": 0.3220554292201996,
"beginner": 0.36051270365715027,
"expert": 0.3174319267272949
}
|
8,174
|
hi please help me to solve this problem. #Create a NEW TEXT FILE --> CEIT_News using the open function.
# ------>> Remember that open function will be borrowed from a MODULE
#Write to the file the following message --> Fiji PM is visiting UPNG today. Now close file.
#-------------------------------------------------------------------
#+++++++++++++++++++++++++++++++++++++Q4 (10 marks)+++++++++++++++++++++++++++++++++++++++++++++++++++++++
#Now create a function that will accept just one file name
#Call this function --> READMYFILE
#In this function, have the code that reads a file.
#Use the return keyword in the function
#Now, invoke the function
|
624448889e56f044fd95c9235163f7f9
|
{
"intermediate": 0.3357960879802704,
"beginner": 0.48752734065055847,
"expert": 0.17667657136917114
}
|
8,175
|
Flutter file_picker widget. How to send file picked by this widget to server via multipartformdata?
|
4cbee4c3ab0777f50cdb5cf7eaed2db7
|
{
"intermediate": 0.5912553071975708,
"beginner": 0.15448060631752014,
"expert": 0.25426408648490906
}
|
8,176
|
Deprecated function: Required parameter $form_state follows optional parameter $input в функции require_once()
|
5940f55178b390eb9beab051119f942f
|
{
"intermediate": 0.3887905478477478,
"beginner": 0.3375115990638733,
"expert": 0.2736979126930237
}
|
8,177
|
add tailwind css, make it funky and beautiful and use complex animation
import React from "react";
import { FaArrowUp, FaArrowDown, FaWind } from "react-icons/fa";
import { BiHappy } from "react-icons/bi";
import { MdCompress, MdOutlineWaterDrop } from "react-icons/md";
const Descriptions = ({ weather, units }) => {
const tempUnit = units === "metric" ? "°C" : "°F";
const windUnit = units === "metric" ? "m/s" : "m/h";
const cards = [
{
id: 1,
icon: <FaArrowDown />,
title: "min",
data: weather.temp_min.toFixed(),
unit: tempUnit,
},
{
id: 2,
icon: <FaArrowUp />,
title: "max",
data: weather.temp_max.toFixed(),
unit: tempUnit,
},
{
id: 3,
icon: <BiHappy />,
title: "feels like",
data: weather.feels_like.toFixed(),
unit: tempUnit,
},
{
id: 4,
icon: <MdCompress />,
title: "pressure",
data: weather.pressure,
unit: "hPa",
},
{
id: 5,
icon: <MdOutlineWaterDrop />,
title: "humidity",
data: weather.humidity,
unit: "%",
},
{
id: 6,
icon: <FaWind />,
title: "wind speed",
data: weather.speed.toFixed(),
unit: windUnit,
},
];
return (
<div className="section section__descriptions">
{cards.map(({ id, icon, title, data, unit }) => (
<div key={id} className="card">
<div className="description__card-icon">
{icon}
<small>{title}</small>
</div>
<h2>{`${data} ${unit}`}</h2>
</div>
))}
</div>
);
};
export default Descriptions;
|
8d4b71a5b93f02050143e959c23d241f
|
{
"intermediate": 0.3239353895187378,
"beginner": 0.43338558077812195,
"expert": 0.24267904460430145
}
|
8,178
|
Write a program using foreach loop in Php
Marks: 4
Give answer based on guideline of this link:
https://pdf.ac/2f27NY
|
ec986ea7b47bd41b4f4e5f1740598158
|
{
"intermediate": 0.21233788132667542,
"beginner": 0.5188713669776917,
"expert": 0.2687907814979553
}
|
8,179
|
Modify the program code below in such a way that when entering the start and end block, get the address of the token creator. Use APIKEY
import requests
bscscan_api_key = 'CXTB4IUT31N836G93ZI3YQBEWBQEGGH5QS'
def get_newly_created_contracts(start_block, end_block):
url = f'https://api.bscscan.com/api?module=account&action=txlistinternal&startblock={start_block}&endblock={end_block}&sort=asc&apikey={bscscan_api_key}'
try:
response = requests.get(url)
response.raise_for_status()
except requests.exceptions.RequestException as e:
print(f'Error in API request: {e}')
return []
data = response.json()
if data['status'] == '0':
print(f"Error: {data['result']}")
return []
return [tx for tx in data['result'] if tx['isError'] == '0' and tx['contractAddress'] != '']
def display_new_contracts(start_block, end_block):
contracts = get_newly_created_contracts(start_block, end_block)
if not contracts:
print('No new contracts found.')
else:
print(f'Newly created smart contracts between blocks {start_block} and {end_block}: ')
for contract in contracts:
print(f"Block: {contract['blockNumber']} - Address: {contract['contractAddress']}")
start_block = 28503476 # Replace with your desired start block
end_block = 28503476 # Replace with your desired end block
display_new_contracts(start_block, end_block)
|
4519d2d7316572c2a7f3009d43585de0
|
{
"intermediate": 0.5286992192268372,
"beginner": 0.2874656915664673,
"expert": 0.1838350147008896
}
|
8,180
|
what if you grab these Sun values from wikipedia and try to convert them to rgba or other color values for css? output precalculated estimate color values for css color.: Names Sun, Sol (/ˈsɒl/),[1] Sól, Helios (/ˈhiːliəs/)[2]
Adjectives Solar (/ˈsoʊlər/)[3]
Symbol ☉
Observation data
Mean distance
from Earth 1 AU ≈ 1.496×108 km[4]
8 min 19 s at light speed
Visual brightness (V) −26.74[5]
Absolute magnitude 4.83[5]
Spectral classification G2V[6]
Metallicity Z = 0.0122[7]
Angular size 31.6–32.7 minutes of arc[8]
0.527–0.545 degrees
Orbital characteristics
Mean distance from Milky Way core ≈26,660 light-years
Galactic period (2.25–2.50)×108 yr
Velocity ≈251 km/s (orbit around the center of the Milky Way)
≈ 20 km/s (relative to average velocity of other stars in stellar neighborhood)
≈ 370 km/s[9] (relative to the cosmic microwave background)
Physical characteristics
Equatorial radius 695,700 km,[10]
696,342 km[11]
109 × Earth radii[12]
Equatorial circumference 4.379×106 km[12]
109 × Earth[12]
Flattening 9×10−6
Surface area 6.09×1012 km2[12]
12,000 × Earth[12]
Volume 1.41×1018 km3[12]
1,300,000 × Earth
Mass 1.9885×1030 kg[5]
332,950 Earths[5]
Average density 1.408 g/cm3[5][12][13]
0.255 × Earth[5][12]
Center density (modeled) 162.2 g/cm3[5]
12.4 × Earth
Equatorial surface gravity 274 m/s2[5]
28 × Earth[12]
Moment of inertia factor 0.070[5] (estimate)
Escape velocity
(from the surface) 617.7 km/s[12]
55 × Earth[12]
Temperature Center (modeled): 1.57×107 K[5]
Photosphere (effective): 5,772 K[5]
Corona: ≈ 5×106 K
Luminosity (Lsol) 3.828×1026 W[5]
≈ 3.75×1028 lm
≈ 98 lm/W efficacy
Color (B-V) 0.63
Mean radiance (Isol) 2.009×107 W·m−2·sr−1
Age ≈4.6 billion years (4.6×109 years)[14][15]
Photospheric composition (by mass)
73.46% hydrogen[16]
24.85% helium
0.77% oxygen
0.29% carbon
0.16% iron
0.12% neon
0.09% nitrogen
0.07% silicon
0.05% magnesium
0.04% sulphur
|
1db1a5d020fd895b53502b34dae5c409
|
{
"intermediate": 0.2840522527694702,
"beginner": 0.5579853653907776,
"expert": 0.15796242654323578
}
|
8,181
|
what if you grab these Sun values from wikipedia and try to convert them to rgba or other color values for css? output precalculated estimate color values for css color.: Names Sun, Sol (/ˈsɒl/),[1] Sól, Helios (/ˈhiːliəs/)[2]
Adjectives Solar (/ˈsoʊlər/)[3]
Symbol ☉
Observation data
Mean distance
from Earth 1 AU ≈ 1.496×108 km[4]
8 min 19 s at light speed
Visual brightness (V) −26.74[5]
Absolute magnitude 4.83[5]
Spectral classification G2V[6]
Metallicity Z = 0.0122[7]
Angular size 31.6–32.7 minutes of arc[8]
0.527–0.545 degrees
Orbital characteristics
Mean distance from Milky Way core ≈26,660 light-years
Galactic period (2.25–2.50)×108 yr
Velocity ≈251 km/s (orbit around the center of the Milky Way)
≈ 20 km/s (relative to average velocity of other stars in stellar neighborhood)
≈ 370 km/s[9] (relative to the cosmic microwave background)
Physical characteristics
Equatorial radius 695,700 km,[10]
696,342 km[11]
109 × Earth radii[12]
Equatorial circumference 4.379×106 km[12]
109 × Earth[12]
Flattening 9×10−6
Surface area 6.09×1012 km2[12]
12,000 × Earth[12]
Volume 1.41×1018 km3[12]
1,300,000 × Earth
Mass 1.9885×1030 kg[5]
332,950 Earths[5]
Average density 1.408 g/cm3[5][12][13]
0.255 × Earth[5][12]
Center density (modeled) 162.2 g/cm3[5]
12.4 × Earth
Equatorial surface gravity 274 m/s2[5]
28 × Earth[12]
Moment of inertia factor 0.070[5] (estimate)
Escape velocity
(from the surface) 617.7 km/s[12]
55 × Earth[12]
Temperature Center (modeled): 1.57×107 K[5]
Photosphere (effective): 5,772 K[5]
Corona: ≈ 5×106 K
Luminosity (Lsol) 3.828×1026 W[5]
≈ 3.75×1028 lm
≈ 98 lm/W efficacy
Color (B-V) 0.63
Mean radiance (Isol) 2.009×107 W·m−2·sr−1
Age ≈4.6 billion years (4.6×109 years)[14][15]
Photospheric composition (by mass)
73.46% hydrogen[16]
24.85% helium
0.77% oxygen
0.29% carbon
0.16% iron
0.12% neon
0.09% nitrogen
0.07% silicon
0.05% magnesium
0.04% sulphur
|
b1f70654fd64c211d1f2fc98ab40775a
|
{
"intermediate": 0.2840522527694702,
"beginner": 0.5579853653907776,
"expert": 0.15796242654323578
}
|
8,182
|
why I got error return [StoredLogRecordDto.from_orm(model[0]) for model in model_list]
File "pydantic/main.py", line 579, in pydantic.main.BaseModel.from_orm
pydantic.error_wrappers.ValidationError: 5 validation errors for StoredLogRecordDto
request_datetime
field required (type=value_error.missing)
request_url
field required (type=value_error.missing)
request_method
field required (type=value_error.missing)
response_datetime
field required (type=value_error.missing)
response_status_code
field required (type=value_error.missing)
2023-05-25 09:05:22,145 INFO sqlalchemy.engine.Engine BEGIN (implicit)
2023-05-25 09:05:22,145 INFO sqlalchemy.engine.Engine SELECT log.id, log.request_datetime, log.request_url, log.request_method, log.response_datetime, log.response_status_code, log.response_error_type, log.response_error_number, log.response_error_code, log.phone_number, log.duration
FROM log
LIMIT $1::INTEGER
2023-05-25 09:05:22,145 INFO sqlalchemy.engine.Engine [cached since 274.7s ago] (10,)
2023-05-25 09:05:22,147 INFO sqlalchemy.engine.Engine ROLLBACK
INFO: 127.0.0.1:49370 - "GET /logging/log?limit=10 HTTP/1.1" 500 Internal Server Error
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/uvicorn/protocols/http/httptools_impl.py", line 435, in run_asgi
result = await app( # type: ignore[func-returns-value]
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/uvicorn/middleware/proxy_headers.py", line 78, in __call__
return await self.app(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/fastapi/applications.py", line 276, in __call__
await super().__call__(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/applications.py", line 122, in __call__
await self.middleware_stack(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/middleware/errors.py", line 184, in __call__
raise exc
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/middleware/errors.py", line 162, in __call__
await self.app(scope, receive, _send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 79, in __call__
raise exc
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/middleware/exceptions.py", line 68, in __call__
await self.app(scope, receive, sender)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/fastapi/middleware/asyncexitstack.py", line 21, in __call__
raise e
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/fastapi/middleware/asyncexitstack.py", line 18, in __call__
await self.app(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/routing.py", line 718, in __call__
await route.handle(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/routing.py", line 276, in handle
await self.app(scope, receive, send)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/starlette/routing.py", line 66, in app
response = await func(request)
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/fastapi/routing.py", line 237, in app
raw_response = await run_endpoint_function(
File "/home/boika/PycharmProjects/logging/venv/lib/python3.10/site-packages/fastapi/routing.py", line 163, in run_endpoint_function
return await dependant.call(**values)
File "/home/boika/PycharmProjects/logging/src/routes/logging_router.py", line 31, in get_log
results_list = await repository.get_logs(
File "/home/boika/PycharmProjects/logging/src/db/repository.py", line 41, in get_logs
return mapper.map_model_list_to_dto(result.fetchall())
File "/home/boika/PycharmProjects/logging/src/db/mapper.py", line 37, in map_model_list_to_dto
return [StoredLogRecordDto.from_orm(model[0]) for model in model_list]
File "/home/boika/PycharmProjects/logging/src/db/mapper.py", line 37, in <listcomp>
return [StoredLogRecordDto.from_orm(model[0]) for model in model_list]
File "pydantic/main.py", line 579, in pydantic.main.BaseModel.from_orm
pydantic.error_wrappers.ValidationError: 5 validation errors for StoredLogRecordDto
request_datetime
field required (type=value_error.missing)
request_url
field required (type=value_error.missing)
request_method
field required (type=value_error.missing)
response_datetime
field required (type=value_error.missing)
response_status_code
field required (type=value_error.missing)
here my code async def get_logs(
session: AsyncSession,
limit: Optional[int] = 10,
request_url: Optional[str] = None,
response_status_code: Optional[int] = None,
phone_number: Optional[str] = None,
) -> List[StoredLogRecordDto]:
"""
Get logs from database
**limit**: number of logs to get from database (default=10)
**request_url**: optional filter logs by request_url
**response_status_code**: optional filter logs by response_status_code
**response_error_code**: optional filter logs by response_error_code
**phone_id**: optional filter logs by phone_id
:return: List of StoredLogRecordDto
"""
query = select(log).limit(limit)
if filter:
if request_url is not None:
query = query.where(log.c.request_url == request_url)
if response_status_code is not None:
query = query.where(log.c.response_status_code == response_status_code)
if phone_number is not None:
query = query.where(log.c.phone_number == phone_number)
result = await session.execute(query)
return mapper.map_model_list_to_dto(result.fetchall()) class Log(Base):
__tablename__ = "log"
id: Mapped[int] = mapped_column(primary_key=True)
request_datetime: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), nullable=False)
request_url: Mapped[str] = mapped_column(Text, nullable=False)
request_method: Mapped[str] = mapped_column(String(8), nullable=False)
response_datetime: Mapped[datetime] = mapped_column(TIMESTAMP(timezone=True), nullable=False)
response_status_code: Mapped[int] = mapped_column(SmallInteger, nullable=False)
response_error_type: Mapped[str] = mapped_column(String(2), nullable=False)
response_error_number: Mapped[str] = mapped_column(String(2), nullable=False)
response_error_code: Mapped[str] = mapped_column(String(5), nullable=True)
phone_number: Mapped[str] = mapped_column(String(10), nullable=True)
duration: Mapped[timedelta] = mapped_column(Interval, nullable=False)
log = Log.__table__ class BaseLogRecordDto(BaseModel):
"""
DTO that contains general fields
"""
request_datetime: datetime
request_url: str
request_method: str
response_datetime: datetime
response_status_code: int
phone_number: Optional[str]
class LogRecordDto(BaseLogRecordDto):
"""
DTO that contains response_error_code as single string. In this form we get the data
"""
response_error_code: Optional[str]
class StoredLogRecordDto(BaseLogRecordDto):
"""
DTO that contains response_error_code as three strings. In this form we return the data
"""
response_error_type: Optional[str]
response_error_number: Optional[str]
response_error_code: Optional[str]
class Config:
orm_mode = True
|
8434b3b8a351eb8873e5bc8046eab246
|
{
"intermediate": 0.38809752464294434,
"beginner": 0.4015393555164337,
"expert": 0.21036317944526672
}
|
8,183
|
write a script for ubuntu that will create a text file on double click
|
2ecffbad6786df707697808829f5ad8b
|
{
"intermediate": 0.4616352617740631,
"beginner": 0.18132801353931427,
"expert": 0.3570367693901062
}
|
8,184
|
make me a network based tic toc toe javafx game that connects two clients the first play with X and the second plays with O with comments and ready to play and tell me how to run it and play it with both english and arabic
|
79a94537a0143f6a66f08c509394fbdc
|
{
"intermediate": 0.32425081729888916,
"beginner": 0.11019843071699142,
"expert": 0.5655507445335388
}
|
8,185
|
module Transition_Calculator (CLK, Load, Clear, Data, Count)
input CLK,Load,Clear;
input [9:0] Data;
output reg [4:0] Count;
reg prevBit;
always @(posedge CLK or negedge Clear)
begin
if(!Clear)
Count <=0;
else if (Load&&(prevBit!=Data))
Count<=Count+1;
prevBit<=Data[0];
end
end module
for this verilog code i want a testbench code that will try all 1024 data odds A testbench file to simulate and test your design:
a. You have to give a test case for the Asynchronous Clear,
b. You have to cover all the possible cases for the input Data (1024 different cases)
i. For each case you have to:
1. Select a different value for the input Data
2. Set the Load signal to ‘1’
3. Wait for two clock cycles
c. You have to give a test case to show that no change will occur when the Load
signal is ‘0’.
|
a0dcd5e81c43b58dc5cf5a4951d500b5
|
{
"intermediate": 0.46939757466316223,
"beginner": 0.1903943568468094,
"expert": 0.3402080535888672
}
|
8,186
|
In c++, what is a capture list in a lambda expression?
|
68c495a417b3959ab12a49188f86798d
|
{
"intermediate": 0.24656805396080017,
"beginner": 0.5823936462402344,
"expert": 0.17103826999664307
}
|
8,187
|
Укажи где я ошибся?
def f(x, z):
sum1 = 0
for i in range(len(x)):
n = len(x)-1
sum1 += math.ceil(
23 * pow(z[n + 1 - math.ceil(i / 3)], 2) - 38 * pow(x[math.ceil(i / 4)], 3)
)
return sum1
|
a80bdaf51cacb97d4b9cc824488882b9
|
{
"intermediate": 0.2611403465270996,
"beginner": 0.46779683232307434,
"expert": 0.27106285095214844
}
|
8,188
|
if(printerJ.contains("FilmOrientation")){
auto v = printerJ.value("FilmOrientation").toString().toLower();
if(STRING_CMP(v, "PORTRAIT")){
args.append("--portrait");
}else if(STRING_CMP(v, "LANDSCAPE")){
args.append("--landscape");
}else{
args.append("--default-orientation");
}
}
|
822262b82119df44a83ae1e2c3396cde
|
{
"intermediate": 0.30202412605285645,
"beginner": 0.5246955752372742,
"expert": 0.17328035831451416
}
|
8,189
|
import os,re
import numpy as np
import random
import math
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams["toolbar"] = "None" # 隐藏工具栏
matplotlib.rcParams['font.sans-serif'] = ['SimHei'] # 中文显示
matplotlib.rcParams['axes.unicode_minus'] = False # 负号显示
matplotlib.rcParams['lines.linewidth'] = 0.9 # 线条宽度
linewidth_bak = matplotlib.rcParams['lines.linewidth']
# 定义next_num函数
def get_next_num(mySetting,current_num,current_action):
next_num = current_num+mySetting.actions[current_action]*mySetting.unit
return next_num
def get_valid_actions(mySetting,current_num):
# 取当前状态下的合法动作集合
valid_actions = [a for a in mySetting.actions if
(a*mySetting.unit+current_num) <= mySetting.max_storages]
return valid_actions
class Settings:
def __init__(self):
self.epsilon = 0.1 # 贪婪度 greedy
self.alpha = 0.5 # 学习率
self.gamma = 0.9 # 奖励递减值
self.beta = 0.9
# 学习率初始化为0.7，当训练2万次，每迭代千次便将学习率降低为𝛼∗𝛽，𝛽=0.9
self.trainGate = 20000 # 训练门槛，即需要经过多少步训练后才能达到一定的性能水平或完成训练
self.StepMinus = 1000 # 每次训练时步长的减少量
self.onwaydays = 1 # 提前期为1天，从下单到收到货的时间
self.liftime = 3 # 生命周期为3天
self.overduecost = 1.7 # 过期成本为1.7元，
self.shortagecost = 0.4 # 缺货成本为0.4元
self.holdcost = 0.1 # 持有成本为0.1元
self.profit = 0.6 # 单位利润=售价2.1-订购成本1.5
self.max_storages = 100
self.init_min_val = 0
self.order_point = 14
self.order_volume = 43
self.expect = 60
self.variance = 5
self.train_data_n = 1000
self.unit = 5
# 定义Q表
self.actions = list(range(21)) # action包含从0到20的21个数字
a = self.max_storages+1 # a表示状态数（即最大存储数+1）
b = len(self.actions) # b表示动作数（即0-20）
self.Q_table = np.zeros((a,b)) # 创建一个a行b列的全零矩阵作为Q-table的初始值
self.Q_table[:,:] = self.init_min_val
def getEpsilon(self):
return self.epsilon
def getAlpha(self,num):
return self.alpha
def getGamma(self):
return self.gamma
def getRewards(self,overdue_num,short_num,sale_num,store_num):
all_cost = self.max_storages*self.overduecost
now_cost = self.overduecost*overdue_num + self.shortagecost*short_num + self.holdcost*store_num
rewards = all_cost-now_cost
return rewards
def get_loss_rate(self,overdue_num,short_num):
all_cost = self.max_storages*self.overduecost
now_cost = self.overduecost*overdue_num + self.shortagecost*short_num
loss_rate = now_cost/all_cost
return loss_rate
def get_store_cost(self,overdue_num,short_num,current_num):
now_cost = self.overduecost * overdue_num + self.shortagecost * short_num
now_cost += current_num*self.holdcost # 将当前库存成本加上当前库存量的持有库存
return now_cost*1000 # 将成本转换为千元
class Product:
def __init__(self,lifetime,onwaydays): # 初始化
self.lifetime = lifetime
self.onwaydays = onwaydays
def updateLifeTime(self,days):
self.lifetime -= days
self.onwaydays -= days # lifetime和 onwaydays分别减去传入的days
def isOnTheWay(self): # 判断是否在运输途中
if self.onwaydays > 0:
return True
else:
return False
def isInStoreHouse(self): # 判断是否在库存中
if self.lifetime > 0 and self.onwaydays <= 0:
return True
else:
return False
def isOverDue(self): # 判断是否已经过期
if self.lifetime <= 0:
return True
else:
return False
# 统计商品列表中 isInStoreHouse()方法返回值为True的商品数量
def getInStoreHouseNum(ProductList):
num1 = len(ProductList)
num2 = 0
for i in range(num1):
if ProductList[i].isInStoreHouse():
num2 = num2 + 1
return num2
def getOnTheWayNum(ProductList):
num1 = len(ProductList)
num2 = 0
for i in range(num1):
if ProductList[i].isOnTheWay():
num2 = num2 + 1
return num2
def getOverDueNum(ProductList):
num1 = len(ProductList)
num2 = 0
for i in range(num1):
if ProductList[i].isOverDue():
num2 = num2 + 1
return num2
# 从原始的ProductList列表中删除过期产品
def deleteOverDueProduct(ProductList):
tmplist = ProductList[:]
for product in tmplist:
if product.isOverDue():
ProductList.remove(product)
def AddProductOrder(ProductList,lifetime,onwaydays,num):
for i in range(num):
product = Product(lifetime,onwaydays)
ProductList.append(product)
def updateProductLife(ProductList):
num1 = len(ProductList)
num2 = 0
for i in range(num1):
ProductList[i].updateLifeTime(1)
# 对ProductList列表中的每个产品调用updateLifeTime(1)方法，使产品的寿命增加1天
# 去除掉ProductList中前面num个寿命较低的商品
def CheckOutProduct(ProductList,num):
# 按照每个元素的lifetime属性升序排序ProductList列表，lifetime属性较长的产品会被排到列表前面
ProductList.sort(key=lambda x:x.lifetime)
# 从ProductList中保留前num个产品之后的剩余部分
ProductList = ProductList[num:]
return ProductList
def Train_func(qfile,train_data,max_circle=10000):
mySetting = Settings()
maxDays = max_circle
# 创建一个空列表ProductList，用于存储产品对象
ProductList = []
# 在给定的最大天数maxDays范围内反复遍历训练数据集中的每一天
step = len(train_data)
preAction = 0
preNum = 0
for i in range(maxDays):
# 机器学习，补货
j = i%step # 将变量i除以step的余数赋值给变量j,将i限定在0到step-1之间，循环遍历train_data
buy_num = int(train_data[j])
# int将字符串型数据转换为整数型数据
# 从训练数据集中获取第j天的购买数buy_num
# 清理过期货物
overdue_n = getOverDueNum(ProductList)
deleteOverDueProduct(ProductList)
# 当前状态
current_num = getInStoreHouseNum(ProductList)
if buy_num > current_num:
short_n = buy_num - current_num
else:
short_n = 0
# 当天卖出的货物出库，即将前buy_num - short_n个产品从列表中删除
ProductList = CheckOutProduct(ProductList, buy_num - short_n)
# 重新获取当前状态，准备补货
current_num = getInStoreHouseNum(ProductList)
# 根据贝尔曼方程，更新 Q table 中当前状态-动作对应的 Q value
alpha = mySetting.getAlpha(i)
gamma = mySetting.getGamma()
rewards = mySetting.getRewards(overdue_n,short_n,buy_num - short_n,preNum)
pre_q_v = mySetting.Q_table[preNum, preAction]
mySetting.Q_table[preNum, preAction] += alpha * (rewards + gamma * max(mySetting.Q_table[current_num]) - pre_q_v)
# 随机或贪婪地选择当前动作
if (np.random.uniform() < mySetting.getEpsilon()) or ((mySetting.Q_table[current_num, :] == mySetting.init_min_val).all()):
tmp_a = get_valid_actions(mySetting, current_num)
s = random.randint(0,len(tmp_a)-1)
current_action = mySetting.actions.index(tmp_a[s]) # 探索
else:
valid_actions = [a for a in mySetting.actions if (a * mySetting.unit + current_num) <= mySetting.max_storages]
current_action = mySetting.Q_table[current_num, :len(valid_actions)].argmax() # 利用
preNum = current_num
preAction = current_action
# 补充货物
add_num = mySetting.actions[current_action] * mySetting.unit
AddProductOrder(ProductList,mySetting.liftime,mySetting.onwaydays,add_num)
# 刷新货物生存时间
updateProductLife(ProductList)
fh = open(qfile,"w")
aa = mySetting.Q_table.shape
# 遍历Q_table数组中的每个元素，将其转化为字符串形式
for i in range(aa[0]):
tmp1 = mySetting.Q_table[i].tolist()
# 按照逗号进行连接，并在其末尾添加一个换行符
tmp2 = [str(a) for a in tmp1]
cntStr = ",".join(tmp2)+"\n"
fh.write(cntStr)
fh.close()
def Exe_func1(qfile,outfile,logfile,test_data,init_num):
mySetting = Settings()
ProductList = []
mySetting.Q_table = Read_Q_Data(qfile)
AddProductOrder(ProductList, mySetting.liftime,mySetting.onwaydays, init_num)
updateProductLife(ProductList)
updateProductLife(ProductList)
updateProductLife(ProductList)
lgf = open(logfile,"w")
# 向文件中写入第一行标题，包括需求日期、需求量等信息
lgf.write("日期,需求,存量,过期量,缺货,订货动作,库存变动\n")
fh = open(outfile,"w")
step = len(test_data)
for i in range(step):
buy_num = int(test_data[i]) # 从测试集中获取第i天的购买数buy_num
# 清理过期货物
overdue_n = getOverDueNum(ProductList)
deleteOverDueProduct(ProductList)
# 当前状态
current_num = getInStoreHouseNum(ProductList)
storage_num = current_num
if buy_num > current_num:
short_n = buy_num - current_num
else:
short_n = 0
# 当天卖出的货物出库，即将前buy_num - short_n个产品从列表中删除
ProductList = CheckOutProduct(ProductList, buy_num - short_n)
# 重新获取当前状态，准备补货
current_num = getInStoreHouseNum(ProductList)
valid_actions = [a for a in mySetting.actions if (a * mySetting.unit + current_num) <= mySetting.max_storages]
current_action = mySetting.Q_table[current_num, :len(valid_actions)].argmax() # 利用
# 补充货物
add_num = mySetting.actions[current_action] * mySetting.unit
AddProductOrder(ProductList,mySetting.liftime,mySetting.onwaydays,add_num)
# 刷新货物生存时间
updateProductLife(ProductList)
store_cost = mySetting.get_store_cost(overdue_n,short_n,current_num+add_num)
cntStr = "{0},{1:.2f}\n".format(i, store_cost)
fh.write(cntStr)
loginfo =[str(i),str(buy_num),str(storage_num),str(overdue_n),str(short_n),str(add_num),str(current_num+add_num-storage_num)]
cntStr = ",".join(loginfo) + "\n"
lgf.write(cntStr)
fh.close()
lgf.close()
def Exe_func2(qfile,outfile,logfile,test_data,init_num):
mySetting = Settings()
ProductList = []
mySetting.Q_table = Read_Q_Data(qfile)
AddProductOrder(ProductList, mySetting.liftime,mySetting.onwaydays, init_num)
updateProductLife(ProductList)
updateProductLife(ProductList)
updateProductLife(ProductList)
lgf = open(logfile,"w")
# 向文件中写入第一行标题，包括需求日期、需求量等信息
lgf.write("日期,需求,存量,过期量,缺货,订货动作,库存变动\n")
fh = open(outfile,"w")
step = len(test_data)
for i in range(step):
buy_num = int(test_data[i]) # 从测试集中获取第i天的购买数buy_num
# 清理过期货物
overdue_n = getOverDueNum(ProductList)
deleteOverDueProduct(ProductList)
# 当前状态
current_num = getInStoreHouseNum(ProductList)
storage_num = current_num
if buy_num > current_num:
short_n = buy_num - current_num
else:
short_n = 0
# 当天卖出的货物出库，即将前buy_num - short_n个产品从列表中删除
ProductList = CheckOutProduct(ProductList, buy_num - short_n)
# 重新获取当前状态，准备补货
current_num = getInStoreHouseNum(ProductList)
add_num = 0
if current_num<mySetting.order_point:
add_num = mySetting.order_volume
AddProductOrder(ProductList, mySetting.liftime, mySetting.onwaydays, add_num)
updateProductLife(ProductList)
store_cost = mySetting.get_store_cost(overdue_n, short_n,current_num+add_num)
cntStr = "{0},{1:.2f}\n".format(i, store_cost)
fh.write(cntStr)
loginfo = [str(i),str(buy_num),str(storage_num),str(overdue_n),str(short_n),str(add_num),str(current_num+add_num-storage_num)]
cntStr = ",".join(loginfo) + "\n"
lgf.write(cntStr)
fh.close()
lgf.close()
# 读取文件所有的行
def ReadTxtName(filename, dot=',', encoding="gbk"):
lines = []
if os.path.exists(filename): # 判断filename文件是否存在
pass
else:
return []
with open(filename, 'r', encoding=encoding) as file_to_read:
tmpall = file_to_read.readlines()
for line in tmpall:
tmpstr = line.strip('\n').strip() # 去掉每行字符串开头和结尾的空白字符（包括空格、制表符、换行符等）
if tmpstr.startswith('#'): # 判断tmpstr字符串是否以#开头
continue # 如果是注释行，则继续跳过后面的代码，进行下一轮循环
if len(tmpstr) >0:
line = tmpstr.split(dot) # 将tmpstr字符串按照指定的分隔符dot进行分割
lines.append(line)
return lines
# list为二维列表，row为行，col为列
def GetColumnFromList(list, startrow=0, endrow=None, col=0):
data = []
if endrow is None or endrow > len(list):
endrow = len(list)
for i in range(startrow, endrow): # 遍历列表的行数
data.append(list[i][col]) # 将每行的第“col”列的数据添加到“data”列表
return data
# 判断传入的参数是否为数值类型，如果是就返回 True，否则不返回结果
def is_number(num):
if isinstance(num, str):
pass
else:
return True
# 正则表达式，用于匹配数值类型的字符串形式
pattern = re.compile(r'^[-+]?[-0-9]\d*\.\d*|[-+]?\.?[0-9]\d*$')
result = pattern.match(num)
if result:
return True
else:
return False
# 将字符串列表转化为浮点数列表
def StrListToFloat(list):
data = []
for x in list:
if x != '': # 当前元素不为空字符串
data.append(float(x)) # 转化为浮点数类型
else:
data.append(0)
return data
# 将二维字符串列表转化为浮点数列表
def StrList2ToFloat(list):
rslt = []
for row in list:
data = []
for x in row:
if x != '':
if is_number(x):
data.append(float(x))
else:
data.append(x)
# 如果当前元素为空字符串，则将其填补为默认值 0，并添加到 “data” 列表中
else:
data.append(0)
rslt.append(data)
return rslt
# 绘制出折线图
def DrawLine(logfile1,logfile2):
fig = plt.figure(figsize=(10, 5), dpi=80)
mng = plt.get_current_fig_manager()
left, width = 0.05, 0.90
# 主轴（ax1）的位置和大小，背景颜色
ax1 = fig.add_axes([left, 0.05, width, 0.90], gid='mygroup')
ax1.set_facecolor("White")
# 获取logfile1中的第1列，并取绝对值
lines1 = ReadTxtName(logfile1, ",")
day_col1 = GetColumnFromList(lines1, col=0)
day_col1 = day_col1[:]
day_col1 = StrListToFloat(day_col1)
day_col1 = [abs(aa) for aa in day_col1]
# 获取logfile1中的第2列，并取绝对值
loss_col1 = GetColumnFromList(lines1, col=1)
loss_col1 = loss_col1[:]
loss_col1 = StrListToFloat(loss_col1)
loss_col1 = [abs(aa) for aa in loss_col1]
ax1.plot(day_col1, loss_col1, color = 'green', linestyle='-')
# 获取logfile2中的第1列，并取绝对值
lines2 = ReadTxtName(logfile2, ",")
day_col2 = GetColumnFromList(lines2, col=0)
day_col2 = day_col2[:]
day_col2 = StrListToFloat(day_col2)
day_col2 = [abs(aa) for aa in day_col2]
# 获取logfile2中的第2列，并取绝对值
loss_col2 = GetColumnFromList(lines2, col=1)
loss_col2 = loss_col2[:]
loss_col2 = StrListToFloat(loss_col2)
loss_col2 = [abs(aa) for aa in loss_col2]
ax1.plot(day_col2, loss_col2, color = 'r', linestyle='-')
ylabels = [0,10000,20000,30000,40000,50000,60000,70000,80000] # 设置y轴刻度标签
ax1.set_yticklabels(labels=ylabels,family= 'SimHei',fontsize= 12)
ax1.tick_params(which='major',axis='both',length=8,labelsize=12)
ax1.legend(labels=('Q-Learning 成本', "固定订货成本"), loc='upper right',fontsize=14)
plt.show()
matplotlib.rcParams['lines.linewidth'] = linewidth_bak # 恢复matplotlib默认设置
# 生成训练数据并保存到文件
def Create_Train_Data(datafile):
mysetting = Settings()
mu = mysetting.expect
sigma = mysetting.variance
num = mysetting.train_data_n
np.random.seed(1234)
Train_Data = np.random.normal(mu, sigma, num)
# 生成符合正态分布的随机数（μ为期望，σ为方差，数量为num）
f1 = open(datafile,"w")
for i in range(len(Train_Data)):
cnt = int(Train_Data[i]+0.5)
if i<len(Train_Data)-1:
f1.write(str(cnt)+",")
else:
f1.write(str(cnt))
f1.close()
# 读取训练数据并返回其中的一行数据
def Read_Train_Data(datafile):
data = ReadTxtName(datafile)
return data[0]
# 读取Q值文件并返回二维列表
def Read_Q_Data(datafile):
data = ReadTxtName(datafile)
data1 = StrList2ToFloat(data)
rslt = np.array(data1)
return rslt
if __name__ == "__main__":
datafile = "Train_Data35.csv"
Create_Train_Data(datafile)
train_data = Read_Train_Data(datafile)
qfile = "qfile35.csv"
Train_func(qfile,train_data,200000)
rsltfile1 = "rslt35.csv"
logfile1 = "logfile35.csv"
Exe_func1(qfile,rsltfile1,logfile1,train_data,100)
rsltfile2 = "rslt35_1.csv"
logfile2 = "logfile35_1.csv"
Exe_func2(qfile,rsltfile2,logfile2,train_data,100)
DrawLine(rsltfile1,rsltfile2)
麻烦帮我检查一下代码，并解释其含义和目的，为什么Q 表更新结果图、需求满足𝐿 = (60，5^2)库存成本数据以及Q-leaning&固定订货库存成本对比图都没显示出来
|
c84948a89d85d78469df5b620a301d9e
|
{
"intermediate": 0.2712153196334839,
"beginner": 0.4294741451740265,
"expert": 0.299310564994812
}
|
8,190
|
interface KLineChartProps {
symbol: string;
interval: string;
};
const KLineChart: React.FC<KLineChartProps> = ({symbol, interval}) => {
const ref = useRef<HTMLDivElement>(null);
const [chart, setChart] = useState<Chart|null>(null);
const [candlesticks, setCandlesticks] = useState<KLineData[]>([]);
const [kline, setKline] = useState<KLineData>();
const [waiting, setWaiting] = useState(false);
const {enqueueSnackbar} = useSnackbar();
const [webSocket, setWebSocket] = useState<WebSocket | null>(null);
useEffect(() => {
if (chart) {
chart.applyNewData(candlesticks);
}
}, [chart, candlesticks]);
useEffect(() => {
if (ref && ref.current && !chart) {
const kLineChart = init(ref.current, {styles: chartStyles});
setChart(kLineChart);
}
}, [ref, chart]);
useEffect(() => {
if (kline && chart) {
chart.updateData(kline);
}
}, [kline, chart]);
const connectToWs = () => {
if (webSocket) {
webSocket.close();
}
const newWebSocket = new WebSocket(
`wss://fstream.binance.com/ws/${symbol.toLocaleLowerCase()}@kline_${interval}`
);
newWebSocket.onmessage = (message) => {
const data = JSON.parse(message.data);
const candleData = data.k;
const newCandle = {
timestamp: candleData.t,
open: parseFloat(candleData.o),
high: parseFloat(candleData.h),
low: parseFloat(candleData.l),
close: parseFloat(candleData.c),
volume: parseFloat(candleData.v),
};
setKline(newCandle);
};
setWebSocket(newWebSocket);
};
const fetchCandlesticks = () => {
setWaiting(true);
fetchSymbolsKlines(symbol, interval)?.then((response) => {
setCandlesticks(
response.data.map((candlestick: Array<number | string>) => {
return {
timestamp: dayjs.utc(candlestick[0]).valueOf(),
open: parseFloat(candlestick[1].toString()),
high: parseFloat(candlestick[2].toString()),
low: parseFloat(candlestick[3].toString()),
close: parseFloat(candlestick[4].toString()),
volume: parseFloat(candlestick[5].toString()),
};
})
);
setWaiting(false);
});
};
useEffect(() => {
fetchCandlesticks();
}, [symbol, interval]);
useEffect(() => {
if (candlesticks.length > 0) {
connectToWs();
}
}, [candlesticks]);
return (
<>
{
waiting ? (
<CircularProgress
style={{position: "absolute", marginLeft: "1rem"}}
color="inherit"
size={32}
/>
) : (<></>)
}
<Box
ref={ref}
id={`chart-${symbol}`}
sx={{width: "100%", height: "calc(100% - 50px)"}}
/>
</>
);
};
отключение от бесокетов как-то не так работает. меняется symbol, идет запрос за новой монетой и подключение к новым весокетам, но пока не придут новые данные по новой монете показаны старые данные вебскоетов, хотя прошел fetch запрос за новой монетой. Как-будто то бы отключение от вебсокетов идет после запроса. Проанализируй и пойми почему так и реши проблему
|
fe75274aeabd57af43c3479b71187fb1
|
{
"intermediate": 0.2699039578437805,
"beginner": 0.557131290435791,
"expert": 0.17296473681926727
}
|
8,191
|
In python for windows. Code a function to with the parameter pCommandInput and pPort . pCommandInput is an input string and pPort is an Interger defining a com port. These are passed to the function which via RS"£" sends out the pCommandInput string to an rs232 stream and returns the returned data from a connected rs232 device
|
4792f86fd59df6eda2234a982c413fc1
|
{
"intermediate": 0.4557332694530487,
"beginner": 0.2660231590270996,
"expert": 0.2782435715198517
}
|
8,192
|
In python for windows. Code a function to with the parameter pCommandInput and pPort . pCommandInput is an input string and pPort is an Interger defining a com port. These are passed to the function which via RS"£" sends out the pCommandInput string to an rs232 stream and returns the returned data from a connected rs232 device
|
82bf9d8eec6de1a17971e3e67f788f24
|
{
"intermediate": 0.4557332694530487,
"beginner": 0.2660231590270996,
"expert": 0.2782435715198517
}
|
8,193
|
math.ceil и math.floor в чем разнца
|
5581e6d978a4f02ee0d4ab7033d712a0
|
{
"intermediate": 0.35256630182266235,
"beginner": 0.33671993017196655,
"expert": 0.31071367859840393
}
|
8,194
|
🪲
|
0359aa1c9c4c2ef6f87bc4f666e96671
|
{
"intermediate": 0.33436113595962524,
"beginner": 0.2997223734855652,
"expert": 0.36591649055480957
}
|
8,195
|
Исправь ошибки в коде : import math
def main(x, z):
n = 1;
sum1 = 0
for i in range(len(x) - 1):
if i != 0:
sum1 += math.ceil(23 - pow(z[n - math.ceil(i / 3)], 2)
- 38 * pow(x[math.ceil(i / 4)], 3))
return '{:.2e}'.format(sum1) Примеры результатов вычислений:
f([0.74, -0.63],
[-0.64, -0.32]) = 1.60e+01
f([-0.15, -0.51],
[0.34, 0.51]) = 4.60e+01
f([-0.85, 0.3],
[-0.08, -0.17]) = 9.40e+01
f([-0.94, -0.25],
[0.94, 0.88]) = 1.08e+02
f([0.3, -0.25],
[0.03, -0.66]) = 4.40e+01
|
c375000c501959082c7980f027365b8e
|
{
"intermediate": 0.3421822786331177,
"beginner": 0.4633687138557434,
"expert": 0.19444896280765533
}
|
8,196
|
make some anim for Beetle running around the page in css:
- The beetle moves in a straight line until it reaches a turning point.
- It stops and rotates in place to face the new direction.
- It moves in a straight line again. try correct accordingly: .background {
font-weight: bold;
padding: 0px;
justify-content: center;
align-items: center;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
z-index: -1;
position: relative;
display: flex;
}
.background:before {
content: “🪲”;
font-size: 50px;
position: absolute;
animation: beetle-move 20s linear alternate-reverse infinite;
}
@keyframes beetle-move {
0% {
transform: translate(-50%, -50%) rotate(0deg);
}
10% {
transform: translate(calc(-50% + 200px), -50%) rotate(-90deg);
}
25% {
transform: translate(calc(-50% + 200px), calc(-50% + 200px)) rotate(-180deg);
}
40% {
transform: translate(-50%, calc(-50% + 200px)) rotate(90deg);
}
60% {
transform: translate(calc(-50% - 200px), calc(-50% + 200px)) rotate(-90deg);
}
75% {
transform: translate(calc(-50% - 200px), -50%) rotate(0deg);
}
90% {
transform: translate(-50%, -50%) rotate(90deg);
}
100% {
transform: translate(-50%, calc(-50% - 200px)) rotate(180deg);
}
}
|
d5c98ef93762b081a0ffdba7beab26c1
|
{
"intermediate": 0.34918662905693054,
"beginner": 0.2867593765258789,
"expert": 0.36405396461486816
}
|
8,197
|
make some anim for Beetle running around the page in css:
- The beetle moves in a straight line until it reaches a turning point.
- It stops and rotates in place to face the new direction.
- It moves in a straight line again. try correct accordingly: ".background {
font-weight: bold;
padding: 0px;
justify-content: center;
align-items: center;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
z-index: -1;
position: relative;
display: flex;
}
.background:before {
content: “🪲”;
font-size: 50px;
position: absolute;
animation: beetle-move 20s linear alternate-reverse infinite;
}
@keyframes beetle-move {
0% {
transform: translate(-50%, -50%) rotate(0deg);
}
10% {
transform: translate(calc(-50% + 200px), -50%) rotate(-90deg);
}
25% {
transform: translate(calc(-50% + 200px), calc(-50% + 200px)) rotate(-180deg);
}
40% {
transform: translate(-50%, calc(-50% + 200px)) rotate(90deg);
}
60% {
transform: translate(calc(-50% - 200px), calc(-50% + 200px)) rotate(-90deg);
}
75% {
transform: translate(calc(-50% - 200px), -50%) rotate(0deg);
}
90% {
transform: translate(-50%, -50%) rotate(90deg);
}
100% {
transform: translate(-50%, calc(-50% - 200px)) rotate(180deg);
}
}"
|
41123459df0e25393bd037103f85cac7
|
{
"intermediate": 0.3327903747558594,
"beginner": 0.3182343542575836,
"expert": 0.3489753305912018
}
|
8,198
|
Traceback (most recent call last):
File "C:\Users\bel-i\PycharmProjects\pythonProject7\123.py", line 12, in <module>
print(main([0.74, -0.63], [-0.64, -0.32]))
File "C:\Users\bel-i\PycharmProjects\pythonProject7\123.py", line 8, in main
sum1 = math.ceil(23 - pow(z[len(x)+1 - math.ceil(i/3),2]-38*pow(x[math.ceil(i/4),3])))
TypeError: list indices must be integers or slices, not tuple
|
4b3c08d06fcaa93c3f48df60b222b21a
|
{
"intermediate": 0.3070809245109558,
"beginner": 0.4495449364185333,
"expert": 0.24337416887283325
}
|
8,199
|
OK, so I want to increase the efficency of this R code using multiprocessing only (do not change anything except adding multiprocessing):
mutate <- function(selected_players_df, position_vectors_list, probability) {
n_rows <- nrow(selected_players_df)
selected_players_matrix <- selected_players_df
select_random_player <- function(idx_list, selected_players = NULL) {
repeat {
random_idx <- sample(idx_list, 1)
if (!random_idx %in% selected_players) {
return(random_idx)
}
}
}
for (i in 1:n_rows) {
for (pos_idx in 1:length(position_vectors_list)) {
if (runif(1) <= probability) {
selected_players_matrix[i, pos_idx] <- select_random_player(position_vectors_list[[pos_idx]],
selected_players = selected_players_matrix[i, ])
}
}
}
selected_players_df <- data.frame(selected_players_matrix)
return(selected_players_df)
}
target <- function(row1,penalty=5) {
position_ratings <- c("RWRating", "STRating", "GKRating", "CMRating",
"LWRating", "CDMRating", "LMRating", "CFRating",
"CBRating", "CAMRating", "LBRating", "RBRating",
"RMRating", "LWBRating", "RWBRating")
row1<-as.matrix(row1,nrow=1)
parent_data <- Data[row1, ]
ratings <- parent_data[position_ratings]
ratings_log <- log(ratings)
potential_minus_age <- 0.15*parent_data$Potential - 0.6*parent_data$Age
int_reputation <- parent_data$IntReputation
sumratings<-0
rating_list<-c()
for (i in 1:15){
temp<-ratings_log[i,i]
sumratings<-sumratings+temp
rating_list<-append(rating_list, temp)
}
# Apply constraints
constraint_penalty <- 0
if (sum(parent_data$ValueEUR) > 250000000) {
constraint_penalty <- constraint_penalty + log((sum(parent_data$ValueEUR)-250000000)^penalty)
}
if (sum(parent_data$WageEUR) > 250000) {
constraint_penalty <- constraint_penalty + log((sum(parent_data$WageEUR)-250000)^penalty)
}
if (any(rating_list < 1.2)) {
constraint_penalty <- constraint_penalty + 1.2^penalty
}
potential_minus_age
target_value <- -(sumratings+0.3*sum(potential_minus_age) +sum(int_reputation))+constraint_penalty
return(target_value)
}
for (gen in 1:num_generations) {
parent_pop<- as.data.frame(matrix(NA, n_rows, length(position_vectors)))
for(c in 1:(population_size)){
parent_pop[c,]<-tournament_selection (parents,t_size=2,penalty=penalty)
}
offspring_temp<- as.data.frame(matrix(NA, 1, length(position_vectors)))
for (c in seq(1, population_size, by = 2)){
parent1<-parent_pop[c,]
parent2<-parent_pop[c+1,]
offsprings<-crossover(parent1, parent2,corssover_point)
o1<-unlist(offsprings[1,])
o2<-unlist(offsprings[2,])
offspring_temp<-rbind(offspring_temp,o1)
offspring_temp<-rbind(offspring_temp,o2)
}
offspring_temp <- offspring_temp[-1, ]
parents<- mutate(offspring_temp, position_vectors_list, probability=probability)
row.names(parents) <- 1:population_size
solutions<-apply(parents, 1, target)
idx_sol<-which.min(solutions)[[1]]
temp_best<-parents[idx_sol[[1]],]
temp_target_value<-solutions[idx_sol[[1]]]
if(temp_target_value<=global_best_value){
global_best<-temp_best
global_best_value<-temp_target_value
}
penalty<-penalty+0.5
if(penalty>=9){
penalty<-0
}
}
|
e42ffd6877fa3ddc6ef42794bc8ed676
|
{
"intermediate": 0.36792612075805664,
"beginner": 0.43667715787887573,
"expert": 0.19539667665958405
}
|
8,200
|
write code in python to visualize segmentation output
|
a8ec4014fe9e2d671dd2ca67b3398ae5
|
{
"intermediate": 0.3739067614078522,
"beginner": 0.1645941585302353,
"expert": 0.46149906516075134
}
|
8,201
|
hi, i have a pyspark dataset df. i won't to create a new column c2 containing an string 'hola' plus the value of the colum nc1 in string form
|
c73d985ddd6a0b3c3d51a87cac194ad6
|
{
"intermediate": 0.3929159343242645,
"beginner": 0.22983354330062866,
"expert": 0.3772505521774292
}
|
8,202
|
can you code this in C#: The program will begin by reading a tab
-
delimited text file called people.txt, which you
can download
here
.
After skipping the first line of the file that identifies what each column contains, your program will
proceed to read data about each contact a line at a time. From
the data contained on each line, the
program will create a Contact object and add it to the list of contacts. After reading all contacts from the
file, the program will show how many contacts were read. It will then print a menu of options that will
allow
the user to list all people with a particular last name, all people who hail from a particular state
,
and all people who lie within a particular age range. It will also offer the user the option to write all the
contacts to a file.
Here is what you have t
o do:
1.
Declare a class called Person in a file called Person.cs. A person will have properties that store
the first name, last name, and age, constructors, and a ToString() function. Make sure age is
protected against being set to a negative value. (5 point
s)
2.
Declare an interface called Detailed in a file called Detailed.cs. It should declare one function,
GetDetailedDescription, that implementing classes will override to print out a formatted
detailed description of the object. (3 points)
3.
Declare a class ca
lled Contact in a file called Contact.cs.
a.
Declare to it extend Person and implement Detailed (2 points)
b.
The Contact class must have properties for holding the state, email address, and phone
number, each with appropriate data types (3 points)
c.
It must have
two constructors, one default and one non
-
default (2 points)
d.
It must have a ToString() function that offers a one
-
line, tab
-
delimited listing of the data
for each contact. In other words, it shows the data in the same format that each line of
the input fi
le showed. (2 points)
e.
It must have override the GetDetailedDescription function to enable the object to be
printed according to the sample output(2 points)
4.
Declare a class called ContactWriter that can write one
-
line descriptions of Contact objects to
eith
er text files or binary files. (5 points)
5.
In Program.cs,
a.
Include and use a function called PrintWelcome() that prints a welcome banner (1 point)
b.
Ask the user for the name of the input file. (1 point)
c.
Read the contacts from the input file into a List<Contac
t>. If the file cannot be read,
print a warning message and end the program. Otherwise, show how many contacts
were read. (4 points).
d.
Repeatedly print a menu of options that will enable the user to find a contact by last
name, find a contact by state, find
a contact by age range, or print the contacts to a file.
(2 points)
e.
Include and use a function called ListContactsWithLastName that lists the detailed
descriptions of all contacts with a particular last name (3 points)
f.
Include and use a function called Li
stContactsInState that lists the fancy descriptions of
all contacts who live in a particular state (3 points)
g.
Include and use a function called ListContactsInAgeRange that lists the fancy
descriptions of all contacts whose age lies within a particular rang
e (3 points)
h.
Include and use a function called SaveContactsToFile
that writes the one
-
line description
of each contact to a file the user names in either binary or text format depending on
whether the file name the user specifies ends in a .txt or a .bin extension. Make sure
you use ContactWriter to do this. (5 points)
6.
Include helpful comments in your code (1 point)
7.
Submit your entire solution folder (including sln and cs files) as contact_manager_lastname.zip,
where you replace lastname with your last name (1 point)
|
f3b5f4403f99d7c98dab750b6f7150f7
|
{
"intermediate": 0.31743165850639343,
"beginner": 0.474592387676239,
"expert": 0.20797593891620636
}
|
8,203
|
fopen in gcc
|
7b9224cfab5da194695ed7dbb301608b
|
{
"intermediate": 0.4179595410823822,
"beginner": 0.3146013617515564,
"expert": 0.2674391269683838
}
|
8,204
|
用 self.stop_order = self.buy(price=self.stop_price, exectype=bt.Order.Stop,name='Stop Loss Order')创建的止损单，如何让order.isbuy()为假
|
c0ac23e330515342f63c9479063b9282
|
{
"intermediate": 0.30312734842300415,
"beginner": 0.35748472809791565,
"expert": 0.3393878936767578
}
|
8,205
|
Hi. I need detailed consultation for Dll creation and using in WinAPI C++
|
c1ddf35897d0c3bc1cc370df0a5fc1ee
|
{
"intermediate": 0.7334006428718567,
"beginner": 0.1573966145515442,
"expert": 0.10920266807079315
}
|
8,206
|
I need consultation about in WinAPI C++ available built-in API for cteation tables, for example, create table contains in each row one STATIC, one BUTTON and one EDIT and function creates 100 colls with different STATIC, BUTTON and EDIT "names"
|
18b6bce7aec70873f56d7f262f1df4ba
|
{
"intermediate": 0.8705163598060608,
"beginner": 0.050369251519441605,
"expert": 0.0791144147515297
}
|
8,207
|
I want to update python from 3.9 to 3.10 in centos.
|
0e7acc4224275c028dfe35a77ecd0d1e
|
{
"intermediate": 0.326814740896225,
"beginner": 0.25938883423805237,
"expert": 0.41379639506340027
}
|
8,208
|
Task.h,Task.cpp
下面是Task.h头文件的内容：
#ifndef TASK_H
#define TASK_H
#include <thread>
#include <mutex>
#include <condition_variable>
#include <unordered_map>
class Task {
public:
void start(const int ssrc);
void stop(const int ssrc);
void destroy(const int ssrc);
Void add(ssrc){};
Void rm(ssrc){};
private:
void worker(const int ssrc);
std::unordered_map<int, std::thread> m_threads;
std::mutex m_mutex;
std::unordered_map<int, std::thread> m_stream_threads;
std::mutex m_stream_mutex;
std::unordered_map<int, std::thread> m_ssrc_threads;
std::mutex m_ssrc_mutex;
};
#endif // TASK_H
下面是Task.cpp实现文件的内容：
#include "Task.h"
void Task::start(const int ssrc) {
std::unique_lock<std::mutex> lock(m_mutex);
auto thread_iter = m_threads.find(ssrc);
if (thread_iter == m_threads.end()) {
m_threads[ssrc] = std::thread(&Task::worker, this, ssrc);
m_threads[ssrc].detach();
Add();
}
}
void Task::add(ssrc){
Std::unique_lock<std::mutex> lock(m_stream_mutex);
…
Std::unique_lock<std::mutex> lock(m_ssrc_mutex);
…
}
void Task::rm(ssrc){
Std::unique_lock<std::mutex> lock(m_stream_mutex);
…
Std::unique_lock<std::mutex> lock(m_ssrc_mutex);
…
}
void Task::destroy(const int ssrc) {
std::unique_lock<std::mutex> lock(m_mutex);
auto thread_iter = m_threads.find(ssrc);
if (thread_iter != m_threads.end()) {
m_stop_conditions[ssrc].notify_all();
thread_iter->second.join();
m_threads.erase(thread_iter);
}
}
void Task::worker(const int ssrc) {
while (true) {
…
Rm(ssrc);
}
}
Main.cpp
Int main(){
Task task;
Task.start(1);
Task.start(2);
Task.start(3);
…
Task.start(1000);
Int i=0;
While(1){
Sleep(5);
Task.rm(i);
I++;
}
}
在worker线程，add();rm();方法均有用到m_thread,m_stream_thread,m_ssrc_thread资源，请问work线程内外怎么避免资源死锁？
请优化上面重新，避免多线程死锁
|
bd1945bc6c7219c93451347874ff4f00
|
{
"intermediate": 0.32673758268356323,
"beginner": 0.4499351680278778,
"expert": 0.22332730889320374
}
|
8,209
|
Change this whole loop into function:
Change this whole loop into a function:
crossover_point<-7
population_size <- 100
num_generations <- 20000
tournament_size <- 2
probability<-0.09
penalty<-1
parents<-pop_init
global_best<-pop_init[1,]
global_best_value<-target(global_best)
for (gen in 1:num_generations) {
parent_pop<- as.data.frame(matrix(NA, n_rows, length(position_vectors)))
for(c in 1:(population_size)){
parent_pop[c,]<-tournament_selection (parents,t_size=2,penalty=penalty)
}
offspring_temp<- as.data.frame(matrix(NA, 1, length(position_vectors)))
for (c in seq(1, population_size, by = 2)){
parent1<-parent_pop[c,]
parent2<-parent_pop[c+1,]
offsprings<-crossover(parent1, parent2,corssover_point)
o1<-unlist(offsprings[1,])
o2<-unlist(offsprings[2,])
offspring_temp<-rbind(offspring_temp,o1)
offspring_temp<-rbind(offspring_temp,o2)
}
offspring_temp <- offspring_temp[-1, ]
parents<- mutate(offspring_temp, position_vectors_list, probability=probability)
row.names(parents) <- 1:population_size
solutions<-apply(parents, 1, target)
idx_sol<-which.min(solutions)[[1]]
temp_best<-parents[idx_sol[[1]],]
temp_target_value<-solutions[idx_sol[[1]]]
if(temp_target_value<=global_best_value){
global_best<-temp_best
global_best_value<-temp_target_value
}
penalty<-penalty+0.5
if(penalty>=9){
penalty<-0
}
}
that returns :
global_best_fin<-as.matrix(global_best,nrow=1)
parent_data <- Data[global_best_fin, ]
target(global_best)
Now I want to iterate by num_generations from 1000:1000:50000 using parrallel processing and I also want to collect the results into a dataframe (in which I also need to know from which current num_generations the current output is).
|
53986d3962af59457fe99ba19cf46663
|
{
"intermediate": 0.30183783173561096,
"beginner": 0.46761107444763184,
"expert": 0.2305510938167572
}
|
8,210
|
Sorry but I asked for ready made table API in WIN API
|
057afe759ba2639f21cf1710f32a04a4
|
{
"intermediate": 0.721309244632721,
"beginner": 0.10453637689352036,
"expert": 0.17415441572666168
}
|
8,211
|
import React from ‘react’;
const products = [
{ id: 1, title: ‘Product 1’, price: 10 },
{ id: 2, title: ‘Product 2’, price: 20 },
{ id: 3, title: ‘Product 3’, price: 30 },
];
function ProductList() {
return (
<div>
{products.map(product => (
<div key={product.id}>
<h2>{product.title}</h2>
<p>Price: {product.price}</p>
</div>
))}
</div>
);
}
export default ProductList;
|
39adfebb795329b40af62721ad68be75
|
{
"intermediate": 0.3771798610687256,
"beginner": 0.334516704082489,
"expert": 0.2883034944534302
}
|
8,212
|
Solve using Mathematica. The population growth on an island that has a limited space for growth is governed by the following ODE:
dp/dt= G(max - P) P
where
p = population of the island in year t
Pmax = maximum population of the island = 25,000
G= yearly rate of population growth = 10^-5 per people-year
t = time in year
P(0) = 5,000
Predict the island population after 18 years using:
Heun's Method with a step size of 9 years (10 points)
RK4 with a step size of 18 years (10 points)
Which of the solutions above are you more confident in and why? (5 points)
|
5c6920e30efe36be228871aa47478550
|
{
"intermediate": 0.2632884383201599,
"beginner": 0.19703346490859985,
"expert": 0.5396780967712402
}
|
8,213
|
import time
const (
num_priorities = 6
)
type EventKey = string
type EventListener = fn(data ?data_ptr)
type data_ptr = ?
struct EventEmitter {
mut sync.RwMutex
events map[EventKey][]EventListener
}
fn new_emitter() EventEmitter {
return EventEmitter{ events: {} }
}
fn (mut e EventEmitter) subscribe(event string, listener EventListener) {
e.mut.lock()
defer e.mut.unlock()
key := event
if e.events[key].len == 0 {
e.events[key] = []EventListener{}
}
e.events[key] << listener
}
fn (e EventEmitter) emit(event string, data ?data_ptr, priority int) {
e.mut.lock_shared()
defer e.mut.unlock_shared()
key := event
listeners := e.events[key] or { [] }
priority_queue.add(priority, fn {
for listener in listeners {
listener(data)
}
})
}
struct PriorityQueue {
queues [num_priorities][]fn()
}
fn new_priority_queue() PriorityQueue {
mut pq := PriorityQueue{}
for i := 0; i < num_priorities; i++ {
pq.queues[i] = []fn()
}
return pq
}
fn (mut pq PriorityQueue) add(priority int, task fn()) {
if priority < 0 {
priority = 0
} else if priority >= num_priorities {
priority = num_priorities - 1
}
pq.queues[priority] << task
}
static mut priority_queue := new_priority_queue()
fn listener1(data ?data_ptr) {
if let Some(str) = data.? as string {
println("Listener1: " + str)
}
}
fn listener2(data ?data_ptr) {
if let Some(str) = data.? as string {
println("Listener2: " + str)
}
}
fn main() {
mut emitter := new_emitter()
emitter.subscribe("event1", listener1)
emitter.subscribe("event1", listener2)
mut hello1 := "Hello, world! Priority 1"
emitter.emit("event1", &hello1, 1)
mut hello2 := "Hello, world! Priority 2"
emitter.emit("event1", &hello2, 2)
time.sleep(1 * time.second)
}
ошибка на V
code.v:15:9: error: unexpected name `sync`, expecting `:`
13 |
14 | struct EventEmitter {
15 | mut sync.RwMutex
| ~~~~
16 | events map[EventKey][]EventListener
17 | }
Exited with error status 1
|
3a47556ac9538ab8af0d482923e13628
|
{
"intermediate": 0.3492352068424225,
"beginner": 0.3647547960281372,
"expert": 0.2860099971294403
}
|
8,214
|
<div className='grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 gap-10 mt-10'>
{cards.map(({ id, icon, title, data, unit }) => (
<div key={id} className='bg-white rounded-lg shadow-lg hover:shadow-2xl transform hover:-translate-y-2 transition-all duration-500'>
<div className='p-6 flex items-center justify-center'>
{icon}
<small className='text-gray-500 ml-2 font-medium'>{title}</small>
</div>
<h2 className='text-center text-3xl font-bold'>
<span className='text-5xl font-extrabold'>{data}</span> {unit}
</h2>
</div>
))}
</div>
make above fully responsive and beautiful funky with tailwind css
|
f6bf52cdd299731a0115030727480f49
|
{
"intermediate": 0.45997434854507446,
"beginner": 0.22728459537029266,
"expert": 0.31274107098579407
}
|
8,215
|
分析并优化如下代码，使其文字区域的高度一直，要有title，并保证文字要对齐
HTML:
|
17d144165363e275e02494fc0d722105
|
{
"intermediate": 0.33215680718421936,
"beginner": 0.31919947266578674,
"expert": 0.3486437499523163
}
|
8,216
|
can you write a pine script codes for tading view
|
ab522ff49cf4b38d61bc508847058a08
|
{
"intermediate": 0.393376886844635,
"beginner": 0.3973456621170044,
"expert": 0.20927751064300537
}
|
8,217
|
https://dummyjson.com/products является объектом с компонентами:products, total, skip, limit. получи оттуда компонент products
|
00a64f54b2f7d8d03a750e9b53ee5a36
|
{
"intermediate": 0.306098610162735,
"beginner": 0.37576115131378174,
"expert": 0.31814029812812805
}
|
8,218
|
I've got this function getFutureDates that helps me render days based on the current day so that the next 2 weeks in business days are always shown, but when it's a Saturday is renders only the next 4 days. How can I fix it?
export const addBusinessDayRange = (date: Date) =>
addBusinessDays(date, 10 - getDay(date));
export const getFutureDates = () => {
const now = new Date('2023-05-27');
const twoMondaysFromNow = startOfDay(addDays(nextMonday(now), 7));
const fourMondaysFromNow = startOfDay(addDays(twoMondaysFromNow, 14));
const twoFridaysFromTwoMondays = endOfDay(
addBusinessDayRange(twoMondaysFromNow)
);
const twoFridaysFromFourMondays = endOfDay(
addBusinessDayRange(fourMondaysFromNow)
);
const today = startOfDay(now);
const twoFridaysFromToday = endOfDay(addBusinessDayRange(today));
return {
twoMondaysFromNow,
fourMondaysFromNow,
twoFridaysFromFourMondays,
twoFridaysFromTwoMondays,
today,
twoFridaysFromToday
};
};
|
8ad7465b1d57d4ddc7482662b152369e
|
{
"intermediate": 0.44530898332595825,
"beginner": 0.3046172559261322,
"expert": 0.2500738203525543
}
|
8,219
|
Challenge: How much of a specified candy should we order this week?
We need to know how much of a candy to order.
If we have enough in stock (if inStock is greater than weeklyAverage), we should order zero.
If we don't have enough in stock, we should order two times the weekly average for that candy.
Write a function candyOrderQuantity that takes two arguments:
the inventory array
a candy name (string)
Return the amount of that candy that we should order.
If there is no candy with that name in the array, return 0.
|
513817188a46b564537d385e37381919
|
{
"intermediate": 0.4583238363265991,
"beginner": 0.25426316261291504,
"expert": 0.28741300106048584
}
|
8,220
|
malloc_consolidate(): unaligned fastbin chunk detected - что значит?
|
e027c0ef51e9658e38d8d9631d5d0da2
|
{
"intermediate": 0.3769364655017853,
"beginner": 0.1590871512889862,
"expert": 0.46397635340690613
}
|
8,221
|
how to solve this error in linux :
[root@localhost ~]# yum module install container-tools -y
Updating Subscription Management repositories.
Unable to read consumer identity
This system is not registered to Red Hat Subscription Management. You can use subscription-manager to register.
Error: Problems in request:
missing groups or modules: container-tools
|
6b02602064a75cf81488dc89d2165ab4
|
{
"intermediate": 0.5316078066825867,
"beginner": 0.250644326210022,
"expert": 0.21774786710739136
}
|
8,222
|
Tell me the price of HMS 80:20 steel scrap on the east coast ofTurkey, during the first week of May 2023
|
d6ffe931b90ae904e621cb34ff288cd9
|
{
"intermediate": 0.3379644453525543,
"beginner": 0.31325671076774597,
"expert": 0.3487788438796997
}
|
8,223
|
convert the following Python code to Javascript:
|
d2b09d69d87b25288abaf01562000995
|
{
"intermediate": 0.37141427397727966,
"beginner": 0.3063691556453705,
"expert": 0.32221660017967224
}
|
8,224
|
Using knowledge of loops, if statements, compound conditions,
strings, lists, and binary numbers, Write a program that prompts the user for a string. Similar to Lab 2 Question 4, the program will print
the string back in reverse order, but this time it will only print characters located at index that is divisible
by 2 and divisible by 3 (meaning there is no remainder after division of string index by 2 or after division
by 3).
You must use a “for” loop and only one “if” statement in your solution.
Sample run:
Enter a string: abcdefghijklmnopqrst
smga
|
67d44b9bf1508628c090fa7d7e1d8266
|
{
"intermediate": 0.14853516221046448,
"beginner": 0.7754392623901367,
"expert": 0.07602554559707642
}
|
8,225
|
how to use reload in <frame> in js
|
76bde3e0f1eb9c9704744113ae7a83ef
|
{
"intermediate": 0.5912246108055115,
"beginner": 0.26458749175071716,
"expert": 0.14418789744377136
}
|
8,226
|
write poem about growtopia
|
cb3d1631085419721f8ba87249979a3e
|
{
"intermediate": 0.3616529405117035,
"beginner": 0.34911784529685974,
"expert": 0.28922921419143677
}
|
8,227
|
type Symbol<Values extends string = string> = {
type: 'Symbol';
values: Values;
};
|
aa00a5eb5b433e3c5ef03e8ebf7784c7
|
{
"intermediate": 0.463757187128067,
"beginner": 0.24225887656211853,
"expert": 0.29398399591445923
}
|
8,228
|
type Symbol<Values extends string = string> = {
type: 'Symbol';
values: Values;
};
How to assign variable of the type above to string type variable
|
facf3335b9871d861b940fddcf9d521b
|
{
"intermediate": 0.3655818998813629,
"beginner": 0.3592475354671478,
"expert": 0.27517053484916687
}
|
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