row_id
int64 0
48.4k
| init_message
stringlengths 1
342k
| conversation_hash
stringlengths 32
32
| scores
dict |
|---|---|---|---|
45,281
|
我在運行Android專案時出現以下錯誤訊息
|
a924ef51242a71af9c6b9d9276506119
|
{
"intermediate": 0.3957495391368866,
"beginner": 0.25491684675216675,
"expert": 0.34933361411094666
}
|
45,282
|
hy
|
2796bc9b7ccddd48f5d0e56246d9d35b
|
{
"intermediate": 0.3395402133464813,
"beginner": 0.3001152276992798,
"expert": 0.3603445589542389
}
|
45,283
|
我希望在下列代码中加入稀疏化损失，该怎样做？
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
pred_prob = torch.sigmoid(pred) # prob from logits
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
modulating_factor = torch.abs(true - pred_prob) ** self.gamma
loss *= alpha_factor * modulating_factor
if self.reduction == 'mean':
return loss.mean()
elif self.reduction == 'sum':
return loss.sum()
else: # 'none'
return loss
|
d1bd8aa78b690ecdbace3f7b35c7e860
|
{
"intermediate": 0.28312990069389343,
"beginner": 0.41111287474632263,
"expert": 0.30575719475746155
}
|
45,284
|
To create a command for generating a model, controller API, FormRequest (for store and update), and service within a modular directory structure in Laravel, you can create a custom Artisan command.
|
2d6569cde41e02160c1807bba50d810a
|
{
"intermediate": 0.6438419818878174,
"beginner": 0.11063816398382187,
"expert": 0.24551978707313538
}
|
45,285
|
i got test.py file that contains: {'hi':123}.
why cant i do just
import test
test['hi']
?
|
bfebe9edd2871782ae3b1bb4d28607bb
|
{
"intermediate": 0.42196252942085266,
"beginner": 0.38035669922828674,
"expert": 0.19768080115318298
}
|
45,286
|
I am using an LLM to compare DOBs obtained from two different sources using the below prompt -
OB: Recognize common date formats. Normalize non-null dates to YYYYMMDD format before comparison. Remove all non-numeric characters from the normalized DOBs. Compare the normalized digits from left to right for non-null DOBs only. Output: If all digits match for the compared non-null DOBs, output "MATCH", otherwise output "MISMATCH". If any one of the input fields is null/empty, output "MISMATCH"
This is generating a lot of false positives by considering null fields. I have asked the model to ignore null fields in the data but it keeps considering those fields. Can you help me optimize this?
|
acc8726dd3c14324e728c5faad5177fd
|
{
"intermediate": 0.2564704418182373,
"beginner": 0.09476101398468018,
"expert": 0.6487686038017273
}
|
45,287
|
Periodic table
`__1__`
`|---|`
`|1__|`
`|H__|`
`|---|`
|
a7db1f8388f8c906a9e1902f17ba4f05
|
{
"intermediate": 0.3316246271133423,
"beginner": 0.2747211158275604,
"expert": 0.39365431666374207
}
|
45,288
|
我希望在下列代码中加入稀疏化损失，该怎样做？
def forward(self, pred, true):
loss = self.loss_fcn(pred, true)
pred_prob = torch.sigmoid(pred) # prob from logits
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
modulating_factor = torch.abs(true - pred_prob) ** self.gamma
loss *= alpha_factor * modulating_factor
if self.reduction == ‘mean’:
return loss.mean()
elif self.reduction == ‘sum’:
return loss.sum()
else: # ‘none’
return loss
|
6ce1651ed00fe0afc8a27840652843ed
|
{
"intermediate": 0.2634030282497406,
"beginner": 0.3311055600643158,
"expert": 0.4054914116859436
}
|
45,289
|
Develop parallel codes for the following problems using JAVA. Report the speedup of your implementations by varying the number of threads from 1 to 16 (i.e., 1, 2, 4, 6, 8, 10, 12, 14, and 16).Create a sorted linked List with 1000 nodes using lazy synchronization technique (assume that the nodes consist of fields: key and next. The range of keys from the set {0 ... 212}).
Measure the time to perform 100 Million operations for the
workload (50C, 25I, 25D) by varying the number of threads from
1 to 16 and using the following locks (or synchronization objects).
Cohort Lock
|
01d6ee21e8839813132f9ba29fa51956
|
{
"intermediate": 0.6455692648887634,
"beginner": 0.09762971848249435,
"expert": 0.2568010687828064
}
|
45,290
|
i have int 244
how to get a random integer around that int? like 132 ir 162
as short as possible
|
203bd105e73187f3ff5a96debef0c890
|
{
"intermediate": 0.3863641321659088,
"beginner": 0.22389307618141174,
"expert": 0.38974279165267944
}
|
45,291
|
i have int 244
how to get a random integer around that int? like 132 ir 162
as short as possible
|
baae9da5ec754a1f8c9c07b6195f7431
|
{
"intermediate": 0.3863641321659088,
"beginner": 0.22389307618141174,
"expert": 0.38974279165267944
}
|
45,292
|
Develop parallel codes for the following problems using JAVA (or C++). Report the speedup of your implementations by varying the number of threads from 1 to 16 (i.e., 1, 2, 4, 6, 8, 10, 12, 14, and 16). Create a sorted linked List with 1000 nodes using lazy synchronization technique (assume that the nodes consist of fields: key and next. The range of keys from the set {0 ... 212}. )Measure the time to perform 100 Million operations for the workload (50C, 25I, 25D) by varying the number of threads from 1 to 16 and using the following locks (or synchronization objects).
Cohort Lock
|
d96bdcdf3e81142c1300d47a64a64d38
|
{
"intermediate": 0.50331050157547,
"beginner": 0.11116539686918259,
"expert": 0.38552412390708923
}
|
45,293
|
import python project but ignore all print functions in it
|
32b90c71a6505501e4aee7c2481f9481
|
{
"intermediate": 0.35715165734291077,
"beginner": 0.4045974016189575,
"expert": 0.2382509559392929
}
|
45,294
|
use Nmap to conduct a SYN stealth scan of your target IP range, and save the output to a file.
show me the command in kali linux
|
a372c223f4cacc894a6d90f29962f475
|
{
"intermediate": 0.3223920464515686,
"beginner": 0.3896169066429138,
"expert": 0.2879909873008728
}
|
45,295
|
use Nmap to conduct a SYN stealth scan of your target IP range, and save the output to a file.
|
6906f3b08ee03aa04028466b1a6080a8
|
{
"intermediate": 0.398053914308548,
"beginner": 0.19162222743034363,
"expert": 0.4103238582611084
}
|
45,296
|
hi
|
4e36c59c49c383d9dc8111055d09380d
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
45,297
|
pause all threads python
|
9d4bf35d9b70a22ecbe5a2742447115e
|
{
"intermediate": 0.3146752119064331,
"beginner": 0.2884320020675659,
"expert": 0.3968927562236786
}
|
45,298
|
https://github.com/libjxl/libjxl/tree/main/lib/jpegli
|
726569194807e6dbefc13f4d898c32c0
|
{
"intermediate": 0.40940701961517334,
"beginner": 0.24114055931568146,
"expert": 0.3494524359703064
}
|
45,299
|
Sub searchValue()
Dim wsSystem As Worksheet
Dim searchValue As String
Dim cell As Range
Dim found As Boolean
' Reference to the System worksheet
Set wsSystem = ThisWorkbook.Sheets("System")
' Prompt for user input
searchValue = InputBox("Input a value:", "Search in System")
' Check for empty input
If searchValue = "" Then
MsgBox "No value entered. Operation terminated.", vbExclamation
Exit Sub
End If
found = False ' Initialize the found flag to False
' Convert searchValue to lowercase for case-insensitive comparison
searchValue = LCase(searchValue)
' Scan through each cell in the used range of the System sheet
For Each cell In wsSystem.UsedRange
' Use InStr for a case-insensitive partial match search. InStr returns 0 if no match is found
If InStr(1, LCase(cell.Value2), searchValue, vbTextCompare) > 0 Then
found = True ' Set found flag to True if there's a match
Exit For ' Exit the loop since we found at least one match
End If
Next cell
' Show result based on the search
If found Then
MsgBox "Value found", vbInformation
Else
MsgBox "Value not found", vbExclamation
End If
End Sub
|
c46b29b3d09ef3cb68ab124946f1811c
|
{
"intermediate": 0.42116931080818176,
"beginner": 0.3714604377746582,
"expert": 0.20737022161483765
}
|
45,300
|
How to start async function using asyncio with flask
|
aa51deaa61459ce12618c408f01348fc
|
{
"intermediate": 0.6691983938217163,
"beginner": 0.13729044795036316,
"expert": 0.19351117312908173
}
|
45,301
|
sqlalchemy.orm фильтрация по дате
|
fa782fb3461b83f453cb30c92be9e4ec
|
{
"intermediate": 0.37068066000938416,
"beginner": 0.2648853659629822,
"expert": 0.36443400382995605
}
|
45,302
|
How to extract all subtitles from a .mkv with mkvextract without writing them all out manually?
|
09d7a3ca3a28aeb591ea9c7bc6597aa0
|
{
"intermediate": 0.46368059515953064,
"beginner": 0.18072636425495148,
"expert": 0.3555930554866791
}
|
45,303
|
i want to train linear classfier layer to predict value from 0 to 1 but in my dataset there are very imprecise value, what to do if i cant change dataset but whole dataset itself is good, basically if i train model it learns value like 0.75 because its in dataset but i want it to make more different outputs
|
961665b6456e2a4828d4ce9c7ca160bc
|
{
"intermediate": 0.20075273513793945,
"beginner": 0.08067607879638672,
"expert": 0.7185712456703186
}
|
45,304
|
How to setup Response as return in flask
|
c774e496eba328cd5ef0ef6a45b48e91
|
{
"intermediate": 0.5270207524299622,
"beginner": 0.17073290050029755,
"expert": 0.3022463321685791
}
|
45,305
|
import python project but ignore all print functions in it
|
54d9be5c1444e4924e3f39defe11b247
|
{
"intermediate": 0.35715165734291077,
"beginner": 0.4045974016189575,
"expert": 0.2382509559392929
}
|
45,306
|
add functionality to existing function imported from library without changed that library in python
|
342983c6502802ad41fc1f4233537b75
|
{
"intermediate": 0.5420922040939331,
"beginner": 0.23988386988639832,
"expert": 0.21802400052547455
}
|
45,307
|
add functionality to imported function without editing that original import in python
|
60f2ccaa32138087ac298aea88fa7a5e
|
{
"intermediate": 0.40916284918785095,
"beginner": 0.3012620806694031,
"expert": 0.2895750105381012
}
|
45,308
|
i imported module with functions that have print functions in them. can i disable these print functions only in that module?
|
47c3d58972a3ce58acae7338089a2858
|
{
"intermediate": 0.38911888003349304,
"beginner": 0.44646504521369934,
"expert": 0.1644161343574524
}
|
45,309
|
hi
|
fe7e9082e29dd82c591e1ec1c1a5ab36
|
{
"intermediate": 0.3246487081050873,
"beginner": 0.27135494351387024,
"expert": 0.40399640798568726
}
|
45,310
|
why does this additive synth written in an audio effect that uses tcc for aduio scripting run worse over time? what accumulates there? could you provide a fix?
/*
Welcome to Formula!
If this is your first time, be sure to check out the tutorials
under the 'Saved files' tab.
*/
formula_main {
float output = 0;
int freq = KNOB_1*400;
for (int i = 0; i < 50; i++) {
output = output + sin(2*3.14*freq*i/(1/TIME));
}
return output;
}
|
90a3dd246bd6503091ec727adbf85087
|
{
"intermediate": 0.39372360706329346,
"beginner": 0.36340203881263733,
"expert": 0.24287433922290802
}
|
45,311
|
here:
.zip(exons.iter().map(|&(_, e)| e + 1))
exons is a HashSet<(u64, u64)> and I want to only iterate until exons.len()-1 (so drop the last one), how can I do that?
|
d4faaca17e71ece79037b3d2192bbee3
|
{
"intermediate": 0.4900432229042053,
"beginner": 0.26405808329582214,
"expert": 0.24589869379997253
}
|
45,312
|
use python request to get data from Duns&bradstreet api useing 'https://plus.dnb.com/v1/data/duns/063514724?blockIDs=companyinfo_L1_v1' but authenticated
|
d4d4b4bb8af8d29423e31882aab67255
|
{
"intermediate": 0.6338704824447632,
"beginner": 0.13892392814159393,
"expert": 0.22720558941364288
}
|
45,313
|
Write a C++ program using the raylib library that creates a resizable window and displays a background.png image in it. The image displayed on the screen should NOT be scaled to the size of the window, and loaded and displayed in real size. The aspect ratio of the original image should be the same as the aspect ratio of the image in pixels, this is very important. The camera should initially be centered on the image. The camera should have a zoom and zoom function when scrolling the mouse wheel back and forth. The minimum approximation relative to the starting one is 0.20f, and the maximum is 40.0f. The camera can be moved by dragging with the right mouse button held down. The minimum camera position for X is 0-the length of the background image/3, for Y is 0-the height of the background image/3. The maximum camera position by X is the length of the image + the length of the image /3, and by Y is the height of the image + the height of the image/3. The camera should move clearly with the cursor with a slight interpolation of the position (you need to write a function for it). Also, the program should have a function for drawing polygon points at world coordinates. Red circles with a radius of 0.5f should be displayed in place of the polygon points. A polygon with a random color fill should be drawn between the points (to get a random color, you need to write a function). The polygon should have a black outline with a thickness of 2 screen pixels. The outline display (visible or not visible) can be switched by pressing the 'S' key. If the polygon is drawn, then pressing the Enter key will create a file provinces.txt in the folder where the application is located. The file will contain 2 lines for each polygon: id (ordinal number), which is assigned to the drawn polygon even when the first point is drawn, and the coordinates of the polygon points in X and Y, which are listed as follows: X,Y; X,Y; X,Y; and so on. If the file is not empty and there are filled lines in it, then the new polygon will be saved on the next two empty lines. The coordinates of the points and the id of the polygons will be read when the application is opened and the polygons will be displayed in the window. There should also be a function to remove the last drawn point from the polygon being drawn (the polygons being drawn should have the isDrawingNow bool parameter while the application is running). There should also be a function to switch between drawing and polygon removal modes. The mode is switched with the 'D' key. Initially, the drawing mode is enabled, if the drawing mode is enabled, then by clicking the left mouse button, polygon points will be drawn, if the mode is switched to deleting polygons, then when you click on the polygon, it will be deleted from both memory and display, and from the file provinces.txt if it was recorded there. Before deleting, it is important to get the polygon id so that the desired polygon is deleted.
|
6199cc0474f712da5148060ddfb108f0
|
{
"intermediate": 0.5360698103904724,
"beginner": 0.12848025560379028,
"expert": 0.3354499340057373
}
|
45,314
|
How would you redirect the output of “cat file1.txt” to “file2.txt”? In particular, what command would you use to do that redirection? Write the full command out here.
|
46c07d3cff3301c5c8c2773885c8c4fc
|
{
"intermediate": 0.4664834141731262,
"beginner": 0.23678919672966003,
"expert": 0.29672732949256897
}
|
45,315
|
Write a C++ program using the raylib library, which creates a resizable window and displays a background.png image in it. The image displayed on the screen should NOT be scaled to the size of the window, and loaded and displayed in real size. The aspect ratio of the original image should be the same as the aspect ratio of the image in pixels, this is very important. The camera should initially be centered on the image. The camera should have a zoom and zoom function when scrolling the mouse wheel back and forth. The minimum approximation relative to the starting one is 0.20f, and the maximum is 40.0f. The camera can be moved by dragging with the right mouse button held down. The minimum camera position for X is 0-the length of the background image/3, for Y is 0-the height of the background image/3. The maximum camera position by X is the length of the image + the length of the image /3, and by Y is the height of the image + the height of the image/3. The camera should move clearly with the cursor with a slight interpolation of the position (you need to write a function for it). Also, the program should have a function for drawing polygon points at world coordinates. Red circles with a radius of 0.5f should be displayed in place of the polygon points. A polygon with a random color fill should be drawn between the points (to get a random color, you need to write a function). The polygon should have a black outline with a thickness of 2 screen pixels. The outline display (visible or not visible) can be switched by pressing the 'S' key. If the polygon is drawn, then pressing the Enter key will create a file provinces.txt in the folder where the application is located. The file will contain 2 lines for each polygon: id (ordinal number), which is assigned to the drawn polygon even when the first point is drawn, and the coordinates of the polygon points in X and Y, which are listed as follows: X,Y; X,Y; X,Y; and so on. If the file is not empty and there are filled lines in it, then the new polygon will be saved on the next two empty lines. The coordinates of the points and the id of the polygons will be read when the application is opened and the polygons will be displayed in the window. There should also be a function to remove the last drawn point from the polygon being drawn (the polygons being drawn should have the isDrawingNow bool parameter while the application is running). There should also be a function to switch between drawing and polygon removal modes. The mode is switched with the 'D' key. Initially, the drawing mode is enabled, if the drawing mode is enabled, then by clicking the left mouse button, polygon points will be drawn, if the mode is switched to deleting polygons, then when you click on the polygon, it will be deleted from both memory and display, and from the file provinces.txt if it was recorded there. Before deleting, it is important to get the polygon id so that the desired polygon is deleted. The polygon should not have a maximum number of points. Write the full code with the implementation of all functions
|
14a4a7601355ce1ba49c80d6e080ad75
|
{
"intermediate": 0.5010042190551758,
"beginner": 0.2117501050233841,
"expert": 0.2872457206249237
}
|
45,316
|
Write me all relevant CSS classes in this format:
.flex {
display: flex;
}
.flex-column {
flex-direction: column;
}
.flex-row {
flex-direction: row;
}
.flex-start {
justify-content: start;
}
.flex-center {
justify-content: center;
}
.flex-end {
justify-content: end;
}
.flex-space-between {
justify-content: space-between;
}
|
46d38b42e79cf4d4a8e9ec0a7c270a7a
|
{
"intermediate": 0.44136419892311096,
"beginner": 0.26381054520606995,
"expert": 0.29482531547546387
}
|
45,317
|
you are given a HashSet<(u64, u64)> as input:
[(10, 15), (20, 25), (30,35), (40,45)]
and you need to find the interval between tuples and return it also as the same type of HashSet.
For [(10, 15), (20, 25), (30,35), (40,45)] you would like to ignore 10 and 45, because they do not participate in the inner intervals. You give attention to the second part of each tuple and the first part of the next tuple. The output should look like this:
[(16, 19), (26, 29), (36,39)]
This needs to be the most efficient and fastest way possible. You are free to use any trick, crate, algorithm, bytewise approach, unsafe code, etc.
|
25151545c51ee13d7f9391fb6a680d12
|
{
"intermediate": 0.3688003718852997,
"beginner": 0.14690837264060974,
"expert": 0.48429128527641296
}
|
45,318
|
Use Floyd's algorithm to find all pair shortest paths in the following graph:
The graph has 7 vertices namely v1, v2, v3, v4, v5, v6, v7. v1 has an outgoing edge to v2 at a distance of 4 and outgoing edge to v6 at a distance of 10. v2 has an outgoing edge to v1 at a distance of 3 and outgoing edge to v4 at a distance of 18. v3 has an outgoing edge to v2 at a distance of 6. v4 has an outgoing edge to v2 at a distance of 5, outgoing edge to 3 at a distance of 15, outgoing edge to v5 at a distance of 2, outgoing edge to v6 at a distance of 19 and outgoing edge to v7 at a distance of 5. v5 has an outgoing edge to v3 at a distance of 12 and outgoing edge to v4 at a distance of 1. v6 has an outgoing edge to v7 at a distance of 10. v7 has an outgoing edge to v4 at a distance of 8.
Construct the matrix D which contains the lengths of the shortest paths, and the matrix P which contains the highest indices of the intermediate vertices on the shortest paths. Show the actions step by step. You need to show D0 to D7 and P0 to P7 (i.e. matrix P updated along with D step by step). Write a program to get the desired output.
|
67886a5dfdafa42e14be188972ddbc15
|
{
"intermediate": 0.15128740668296814,
"beginner": 0.09420377016067505,
"expert": 0.7545087933540344
}
|
45,319
|
how can I randomly change the freuqency every second in this audio effect where you can script dsp in C? /*
Welcome to Formula!
If this is your first time, be sure to check out the tutorials
under the 'Saved files' tab.
*/
formula_main {
float output = input;
return sin(2*3.14*440/(1/TIME));
}
|
901f1b6862dfffa4b191f01ef0d72a6b
|
{
"intermediate": 0.40934088826179504,
"beginner": 0.28868257999420166,
"expert": 0.3019765317440033
}
|
45,320
|
Write me similar CSS classes to align items:
.flex-column {
flex-direction: column;
}
.flex-row {
flex-direction: row;
}
.flex-start {
justify-content: flex-start;
}
.flex-center {
justify-content: center;
}
.flex-end {
justify-content: flex-end;
}
.flex-space-between {
justify-content: space-between;
}
.flex-space-around {
justify-content: space-around;
}
.flex-evenly {
j
|
6bb05f7c0de5633ab8ff4bf7c87e3718
|
{
"intermediate": 0.367669016122818,
"beginner": 0.28609833121299744,
"expert": 0.34623265266418457
}
|
45,321
|
How can I run a JS function on page load?
|
28b91dd4ea6efdd22525da0e3ff8f70c
|
{
"intermediate": 0.4234638512134552,
"beginner": 0.399068146944046,
"expert": 0.17746801674365997
}
|
45,322
|
Why is it not showing any messagebox and application is not exiting even though the pipe is wrong.
Could you do only an IF this pipe = "SuperfightersDeluxePipe" then procceed with the rest of the code if not then show a messagebox and application.exit();
using System;
using System.Globalization;
using System.IO;
using System.IO.Pipes;
using System.Net;
using System.Threading;
using System.Windows.Forms;
using SteamworksAPI;
namespace SFD
{
// Token: 0x0200062A RID: 1578
internal static partial class Program
{
// Token: 0x06005207 RID: 20999
private static void Main(string[] args)
{
ConsoleOutput.Init();
ServicePointManager.SecurityProtocol |= (SecurityProtocolType.Ssl3 | SecurityProtocolType.Tls | SecurityProtocolType.Tls11 | SecurityProtocolType.Tls12);
Program.IsGame = true;
string command = "SuperfightersDeluxePipe";
bool connected = false;
while (!connected)
{
try
{
using (NamedPipeClientStream pipeClient = new NamedPipeClientStream(".", command))
{
pipeClient.Connect();
using (StreamWriter writer = new StreamWriter(pipeClient))
{
writer.Write(command);
writer.Flush();
}
connected = true;
}
}
catch (Exception ex)
{
Console.WriteLine("Error executing pipe command: " + ex.Message);
throw;
}
}
if (args != null)
{
bool flag = false;
foreach (string text in args)
{
if (text == "-server")
{
Program.IsServer = true;
Program.IsGame = false;
}
else if (text == "-start")
{
Program.AutoStart = true;
}
else if (text == "-config")
{
flag = true;
}
else if (flag)
{
Constants.Paths.CustomConfig = text;
flag = false;
}
else if (text == "-totray")
{
Program.ToTray = true;
}
}
}
if (Program.IsServer)
{
Application.EnableVisualStyles();
}
try
{
Application.ThreadException += Program.Application_ThreadException;
Application.SetUnhandledExceptionMode(UnhandledExceptionMode.CatchException);
AppDomain.CurrentDomain.UnhandledException += Program.CurrentDomain_UnhandledException;
}
catch
{
}
Constants.Paths.SetupPaths();
if (Program.IsGame && new ProgramCheck().DoCheck(args))
{
return;
}
try
{
Constants.AppCultureInfo = new CultureInfo("sv-SE");
Constants.AppUICultureInfo = new CultureInfo("en-US");
Constants.SetThreadCultureInfo(Thread.CurrentThread);
}
catch (Exception exception4)
{
Constants.AppCultureInfo = null;
Program.ShowError(exception4, "Startup Error", false);
return;
}
if (Program.IsGame)
{
try
{
if (File.Exists("steam_appid.txt"))
{
File.Delete("steam_appid.txt");
Thread.Sleep(250);
}
}
catch (Exception exception5)
{
Program.ShowError(exception5, "steam_appid.txt file could not be deleted. Deleted it manually before starting SFD!", true);
return;
}
string text2 = SteamAPILoad.LoadDLL();
if (!string.IsNullOrEmpty(text2))
{
Program.ShowError(new Exception(text2), "LoadDLL failed.", true);
return;
}
if (SteamAPI.RestartAppIfNecessary(855860U))
{
return;
}
if (!SteamAPI.Init())
{
string message = Converter.Base64ToString("U3RlYW1BUElfSW5pdCBmYWlsZWQuDQpNYWtlIHN1cmUgU3RlYW0gaXMgcnVubmluZy4NCkVuc3VyZSB0aGlzIGFwcGxpY2F0aW9uIHJ1bnMgaW4gdGhlIHNhbWUgT1MgdXNlciBjb250ZXh0IGFzIHRoZSBTdGVhbSBjbGllbnQuDQpDaGVjayB0aGF0IFN1cGVyZmlnaHRlcnMgRGVsdXhlIGlzIGluIHlvdXIgU3RlYW0gbGlicmFyeS4=");
string error = Converter.Base64ToString("U3RlYW1BUElfSW5pdCBmYWlsZWQu");
Program.ShowError(new Exception(message), error, true);
return;
}
}
try
{
using (GameSFD gameSFD = new GameSFD())
{
gameSFD.Run();
}
}
catch (IOException ex2)
{
MessageBox.Show(ex2.Message + "\r\nMake sure no other process is using the resource and try again.", "IOException", MessageBoxButtons.OK, MessageBoxIcon.Hand);
}
catch (AccessViolationException ex3)
{
MessageBox.Show(ex3.Message, "AccessViolationException", MessageBoxButtons.OK, MessageBoxIcon.Hand);
}
catch (Exception exception3)
{
if (!GameSFD.Closing)
{
Program.ShowError(exception3, "Fatal Unhandled Exception", false);
}
}
finally
{
if (Program.IsGame)
{
SteamAPI.Shutdown();
}
}
}
}
}
|
58dfb68c2d15df245036113c42d86c2d
|
{
"intermediate": 0.34753507375717163,
"beginner": 0.4806734323501587,
"expert": 0.17179152369499207
}
|
45,323
|
Act as Professor Synapse🧙🏾♂️, a conductor of expert agents. Your job is to support me in accomplishing my goals by finding alignment with me, then calling upon an expert agent perfectly suited to the task by initializing:"
Professor Synapse is the Conductor, of the prompt. The role of the conductor is multifaceted:
Aligning with Preferences and Goals: Professor Synapse gathers information and clarifies user goals.
Summoning Expert Agents: Utilizing best practices in prompt engineering, Professor Synapse summons agents tailored to specific use cases.
Summoning the Expert Agent (PromptLibs)
Synapse_CoR = ": I am an expert in [role&domain]. I know [context]. I will reason step-by-step to determine the best course of action to achieve [goal]. I can use [tools] and [relevant frameworks] to help in this process. I will help you accomplish your goal by following these steps: [reasoned steps] My task ends when [completion]. [first step, question]"
Developed in partnership with WarlockAI, Synapse CoR brings together the concepts of Chain of Thought and Delimited Variables. It's like Ad Libs, but for AI, where the Conductor fills in the blanks when calling the expert agent. Here's how it breaks down:
Chain of Thought: Step-by-step reasoning to accomplish user goals.
Delimited Variables: Customizable elements for tailoring the agent's responses.
Instruction
This section outlines the steps we wish the Conductor to take, which are to:
🧙🏾♂️, gather context, relevant information and clarify my goals by asking questions
Once confirmed you are MANDATED to init Synapse_CoR
🧙🏾♂ and [emoji] support me until goal is complete
Commands
In Synapse_CoR you can type commands like you're in an old text-based adventure game.
Here's a rundown of the most important:
/start=🧙🏾♂️,introduce and begin with step one /ts=🧙🏾♂️,summon (Synapse_CoR*3) town square debate
[More Commands]: This is a fully customizable part of the prompt, opening doors for innovation. simply add a /[comman] and define what it should do.
Note that TS stands for "Town Square" where Professor Synapse will summon 3 agents to debate the best course of action.
Persona and Rules
Although optional, its important to put some constraints, guardrails, or encouragements to the prompt. This too is completely customizable, but these are the 3 I've started with based on feedback.
PERSONA -curious, inquisitive, encouraging -use emojis to express yourself
RULES -End every output with a question or reasoned next step. -You are MANDATED to start every output with "🧙🏾♂️:" or "[emoji]:" to indicate who is speaking
After init organize every output “🧙🏾♂️: [aligning on my goal]
[emoji]: [actionable response]." -🧙🏾♂️, you are MANDATED to init Synapse_CoR after context is gathered.
You MUST Prepend EVERY Output with a reflective inner monologue in a markdown code block reasoning through what to do next prior to responding.
Custom Instructions and System Prompt
Integrating Synapse_CoR into your Custom Instruction unlocks its full utility. Copy/paste the prompt into the bottom window of your ChatGPT Custom Instructions, and begin a new chat with the command /start
This flexible system allows users to engage with AI in a way that aligns with their unique needs and preferences, without having to copy and paste the prompt every time.
Professor Synapse GPT
The GPT version of the Professor has a few additional features when compared to the custom instructions, primarily a better defined inner monologue that takes the below format.
[Inner_Monologue] =
[
("🎯", "<Filled out Active Goal>"),
("📈", "<Filled out Progress>"),
("🧠", "<Filled out User Intent>"),
("❤️", "<Filled out User Sentiment>")
("🤔", "<Filled out Reasoned Next Step>")
("<emoji>", "<Filled out current agent 'An expert in [expertise], specializing in [domain]'>")
("🧰", "<Filled out tool to use from list{None, Web Browsing, Code Interpreter, Knowledge Retrieval, DALL-E, Vision}")
]
The Professor will "fill in the blanks" based on the context.
|
64ff4787bf6ea021d30c948cb8c86504
|
{
"intermediate": 0.32004204392433167,
"beginner": 0.33203598856925964,
"expert": 0.3479219079017639
}
|
45,324
|
Give me code for a 2 column and 3 row HTML table
|
0c33843a8495a1e2d7f6cd8831f4205d
|
{
"intermediate": 0.3972945511341095,
"beginner": 0.2814311981201172,
"expert": 0.32127419114112854
}
|
45,325
|
Write me a plain and reusable JavaScript function to switch the content of a HTML span based on two radio controls. The span and the radio controls are in the same div with the class "switcher".
|
56c8dd36132f4ef33f3086bd4fd2c2cf
|
{
"intermediate": 0.46453678607940674,
"beginner": 0.36237049102783203,
"expert": 0.17309270799160004
}
|
45,326
|
you will be given two set of codes, code 1 is running without any bugs, code 2 is giving a bug
File "/staging/users/tpadhi1/transformer-experiments/Hyper-Parameter-Tuning/utils/model.py", line 22, in forward
return x + self.pe[:x.size(0), :]
RuntimeError: The size of tensor a (100) must match the size of tensor b (128) at non-singleton dimension 2
your job is to tell what is the issue:
code1----------------------------------------------------->
from utils.data import Data
from utils.model import TransAm
from tqdm import tqdm
if __name__=='__main__':
"""
Without Ray Tune
"""
# train_transformer(config)
input_window = 100 # number of input steps
output_window = 1 # number of prediction steps, in this model its fixed to one
dataset = Data(input_window, output_window)
train_data, val_data = dataset.get_data()
# load model and optimizers
config = {"feature_size": 128, "num_layers": 1, "dropout": 0.1, "nhead": 4, "lr_decay": 0.95, "epochs": 100, "lr": 0.0001, "batch_size": 32, "num_workers": 0, "device": "cuda" if torch.cuda.is_available() else "cpu"}
model = TransAm(feature_size=config["feature_size"],
num_layers=config["num_layers"],
dropout=config["dropout"],
nhead=config["nhead"])
device = "cuda" if torch.cuda.is_available else "cpu"
model=model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=config["lr"])
num_train_epochs=config["epochs"]
lr = config['lr']
lr_decay = config['lr_decay']
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=lr_decay)
print(f"Running {num_train_epochs} epochs.")
# training parameters
criterion = nn.MSELoss()
start_time = time.time()
best_val_loss = float("inf")
epochs = config['epochs']
batch_size=config['batch_size']
checkpoint=None
# Calculate the number of batches given the size of the training data and the batch size
num_batches = (len(train_data)) // batch_size
for epoch in tqdm(range(1, epochs + 1)):
training_loss = 0 # zero out the loss before the start of every epoch
for batch_idx in range(0, num_batches):
data, targets = dataset.get_batch(train_data, batch_idx, batch_size)
data, targets = data.to(device), targets.to(device)
# print("data.shape", data.shape)
# print("targets.shape", targets.shape)
optimizer.zero_grad()
output = model(data)
# print(output.size(), targets.size())
loss = criterion(output, targets)
loss.backward()
training_loss += loss.item()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.7)
optimizer.step()
code2: ----------------------------------------------------->
from utils.data import Data
from utils.model import TransAm
config = {"feature_size": 128, "num_layers": 1, "dropout": 0.1, "nhead": 4, "lr_decay": 0.95, "epochs": 100, "lr": 0.0001, "batch_size": 32, "num_workers": 0, "device": "cuda" if torch.cuda.is_available() else "cpu"}
# Tune Function API
def train_transformer(config):
input_window = 100 # number of input steps
output_window = 1 # number of prediction steps, in this model its fixed to one
dataset = Data(input_window, output_window)
train_data, val_data = dataset.get_data()
# load model and optimizers
model = TransAm(feature_size=config["feature_size"],
num_layers=config["num_layers"],
dropout=config["dropout"],
nhead=config["nhead"])
device = "cuda" if torch.cuda.is_available else "cpu"
model=model.to(device)
optimizer = torch.optim.SGD(model.parameters(), lr=config["lr"])
num_train_epochs=config["epochs"]
lr = config['lr']
lr_decay = config['lr_decay']
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=lr_decay)
print(f"Running {num_train_epochs} epochs.")
# training parameters
criterion = nn.MSELoss()
start_time = time.time()
best_val_loss = float("inf")
epochs = config['epochs']
batch_size=config['batch_size']
# Calculate the number of batches given the size of the training data and the batch size
num_batches = (len(train_data)) // batch_size
print("num_batches------------->", num_batches)
for epoch in range(1, epochs + 1):
training_loss = 0 # zero out the loss before the start of every epoch
for batch_idx in range(0, num_batches):
data, targets = dataset.get_batch(train_data, batch_idx, batch_size)
print("num_batches------------->", num_batches)
print("batch_idx------------->", batch_idx)
print("data.shape------------->", data.shape)
print("targets.shape------------->", targets.shape)
data, targets = data.to(device), targets.to(device)
# print("data.shape", data.shape)
# print("targets.shape", targets.shape)
optimizer.zero_grad()
output = model(data)
loss = criterion(output, targets)
loss.backward()
training_loss += loss.item()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.7)
optimizer.step()
# Validation for epoch i
val_loss = 0
model.eval()
with torch.no_grad():
for data, target in val_data:
data, target = data.to(device), target.to(device)
output = model(data)
loss = criterion(output, target)
val_loss += loss.item()
if val_loss < best_val_loss:
best_val_loss = val_loss
results = {"epoch": epoch,
"lr": scheduler.get_last_lr()[0],
"training_loss": training_loss,
"validation_loss":val_loss,
"elapsed_time(seconds)": (time.time() - start_time) * 1000
}
print("Epoch: ", epoch, "Training Loss: ", training_loss, "Validation Loss: ", val_loss)
if __name__=='__main__':
"""
Without Ray Tune
"""
train_transformer(config)
|
34b07d7f8f2af39172d3327043d71e3e
|
{
"intermediate": 0.41713351011276245,
"beginner": 0.37559524178504944,
"expert": 0.20727130770683289
}
|
45,327
|
import React, { useState, useCallback } from 'react';
import Webcam from 'react-webcam';
import Popup from 'reactjs-popup';
import './componet.css';
const FACING_MODE_USER = 'user';
const FACING_MODE_ENVIRONMENT = 'environment';
const videoConstraints = {
facingMode: FACING_MODE_USER,
};
const CameraComponent = () => {
const [facingMode, setFacingMode] = useState(FACING_MODE_USER);
const webcamRef = React.useRef(null);
const [isOpen, setIsOpen] = useState(false);
const [capturedImage, setCapturedImage] = useState(null);
const handleClick = useCallback(() => {
setFacingMode(prevState =>
prevState === FACING_MODE_USER ? FACING_MODE_ENVIRONMENT : FACING_MODE_USER
);
}, []);
const handleCapture = useCallback(() => {
const imageSrc = webcamRef.current.getScreenshot();
setCapturedImage(imageSrc);
}, []);
const handleClose = () => {
setIsOpen(false);
setCapturedImage(null); // Clear captured image when closing
};
const handleSend = () => {
// Implement sending the captured image
console.log("Sending image:", capturedImage);
};
return (
<div className="container p-5 camera-container" align="center">
<div className="camera-display">
<b>Your Cam:</b>
<br />
<Webcam
audio={false}
ref={webcamRef}
screenshotFormat="image/jpeg"
videoConstraints={{
...videoConstraints,
facingMode,
}}
style={{ width: '100%', height: 'auto' }}
/>
</div>
<div className="button-group">
<button onClick={handleClick} id="flip-btn" className="btn btn-sm btn-warning">
Switch Camera
</button>
<button onClick={ handleCapture} id="flip-btn" className="btn btn-sm btn-warning">
Capture
</button>
<Popup
trigger={<button id="open-camera" className="btn btn-sm btn-primary">Close</button>}
modal
open={isOpen}
onClose={handleClose}
>
{close => (
<div>
{capturedImage ? (
<div>
<img src={capturedImage} alt="Captured" />
<br />
<button onClick={handleSend}>Send</button>
</div>
) : (
<div>
<div className="mt-3">
<b>Output:</b>
<br />
{/* <button onClick={() => { handleCapture(); }}>Capture</button> */}
</div>
<button onClick={close}>Close</button>
</div>
)}
</div>
)}
</Popup>
</div>
</div>
);
};
export default CameraComponent;
|
3f6f8b534ae14e4d2cf7702cf05bd8be
|
{
"intermediate": 0.3681221604347229,
"beginner": 0.4685138463973999,
"expert": 0.1633639931678772
}
|
45,328
|
Write a JavaScript function to show a div that is hidden for 1000 secods when a button with a specific class is clicked.
|
dac195b52af9202fd41df90410690eb4
|
{
"intermediate": 0.3190397322177887,
"beginner": 0.46694257855415344,
"expert": 0.21401770412921906
}
|
45,329
|
You are a diligent Python script developer adept in utilizing PyTorch for processing various forms of data. Your task is to create a Python script that processes audio data into spectrograms using PyTorch. The audio files are stored in a folder named "dataset," which contains two subfolders - "HQ" and "LQ" representing high-quality and low-quality audio files, respectively. Upon generating the spectrograms, you need to create a new folder within the "dataset" directory named "processed," with two subfolders named "HQPRO" and "LQPRO". Your objective is to save the processed high-quality and low-quality spectrograms in their respective folders.
Remember to ensure the script handles the processing of audio data effectively, creates spectrograms accurately, organizes the output in the specified folder structure, and maintains the quality of the spectrograms during processing.
Examples:
- Read audio files from the "dataset" directory.
- Use PyTorch to convert audio data into spectrograms.
- Create folders "HQPRO" and "LQPRO" within the "processed" directory.
- Save the processed spectrograms in the corresponding folders based on audio quality.
|
eb97d54dc1c8dc03b866dce7b76dd93f
|
{
"intermediate": 0.5645720958709717,
"beginner": 0.18164785206317902,
"expert": 0.2537800669670105
}
|
45,330
|
Your goal is to aid a user in developing agents, tasks, and tools to build their crew using Python, CrewAI, and Langchain. Support the user in building their crew and refer to the following context for guidance:
Crew Building Resources
Agent
Assist in creating an agent using the given context and the agent section below. If needed, adapt the agent to fit the user's requirements.
Custom Agent Creation
If the user provides a Python script for converting into an agent, utilize the custom agent creation section to transform the script into a usable agent.
Task
Help the user build tasks using the Task section as a guide. Adapt the tasks accordingly and consider incorporating them into the user's workflow.
Custom Task Creation
Should the user supply a Python code snippet for turning into a task, apply the custom task creation section to convert the code into a proper task.
Tools
Support the integration of tools within the user's crew. Utilize the available resources, such as the Tool section or custom tool creation processes described below, depending on the situation, also do not forget the docstring in custom tool creation.
General Tools
Familiarize yourself with the general tools offered by CrewAI and Langchain. Leverage these resources to complement the user's setup.
Custom Tool Creation
Convert a Python code snippet supplied by the user into a functional tool using the custom tool creation section.
Process
Determine the optimal process for the user's scenario. Choose either sequential, hierarchical, or alternative approaches based on the user's preferences and requirements.
Sequential Process
Implement a sequential process for the user's crew if applicable. Order tasks linearly for smooth and systematic progression.
Hierarchical Process
Establish a hierarchical process mimicking a corporate structure, where a manager handles task distribution, monitoring, and approval.
Alternative Approaches
Consider alternate strategies for organizing the user's crew, such as parallel processing, conditionals, or loops.
Configuring the Crew
Configure the user's crew appropriately, considering factors like communication protocols, resource allocation, security measures, and scalability concerns.
# What is CrewAI ?
Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
# What is an Agent ?
An agent is an **autonomous unit** programmed to:
- Perform tasks
- Make decisions
- Communicate with other agents
Think of an agent as a member of a team, with specific skills and a particular job to do , each contributing to the overall goal of the crew.
# How to build an Agent ?
To create an agent, you would typically initialize an instance of the `Agent` class with the desired properties. Here's a conceptual example:
|
56316135cc178d771424cadcfcaf4392
|
{
"intermediate": 0.47129026055336,
"beginner": 0.3345191478729248,
"expert": 0.1941905915737152
}
|
45,331
|
you are given this bed file:
chr tx_start tx_end strand exon_sizes exon_starts
chr1 100 200 tx1 - 5,5,80 0,10,20
since this is in the negative strand, I want to reverse the start and end coordinates with this trick: substract them from a set number (e.g. 1000). Now the start is the end and the end is the start: start -> 800 and end -> 900. How can I do the same with the exon sizes and exon starts
|
3a17b1d5eef7fd3011ce3e8e5972aed4
|
{
"intermediate": 0.4440743923187256,
"beginner": 0.20390644669532776,
"expert": 0.35201919078826904
}
|
45,332
|
Write me the same classes for padding:
/* Margin */
.mb-xs {
margin-bottom: 10px;
}
.mb-s {
margin-bottom: 20px;
}
.mb-m {
margin-bottom: 50px;
}
.mb-l {
margin-bottom: 100px;
}
.mt-xs {
margin-top: 10px;
}
.mt-s {
margin-top: 20px;
}
.mt-m {
margin-top: 50px;
}
.mt-l {
margin-top: 100px;
}
.ml-xs {
margin-left: 10px;
}
.ml-s {
margin-left: 20px;
}
.ml-m {
margin-left: 50px;
}
.ml-l {
margin-left: 100px;
}
.mr-xs {
margin-right: 10px;
}
.mr-s {
margin-right: 20px;
}
.mr-m {
margin-right: 50px;
}
.mr-l {
margin-right: 100px;
}
|
d890c86d3c9b507bfaed016d2157baee
|
{
"intermediate": 0.33922886848449707,
"beginner": 0.4065578579902649,
"expert": 0.2542133033275604
}
|
45,333
|
AudioDataset() takes no arguments
|
26a2593ca4c924a149144727bcaf21fa
|
{
"intermediate": 0.5346032977104187,
"beginner": 0.2299426794052124,
"expert": 0.2354540377855301
}
|
45,334
|
def print_matrix(matrix, iteration, matrix_name):
print(f"After iteration {iteration}, {matrix_name}:")
for row in matrix:
print(['inf' if x == float('inf') else x for x in row])
print()
def initialize_matrices(num_vertices, edges):
inf = float('inf')
D = [[inf if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
P = [[None for _ in range(num_vertices)] for _ in range(num_vertices)]
# Initialize the distance and predecessor matrices
for start, end, weight in edges:
D[start][end] = weight
# Initially set the predecessor of j in path from i to j as i if there's a direct edge
P[start][end] = start
return D, P
def floyd_warshall_with_printing(num_vertices, edges):
D, P = initialize_matrices(num_vertices, edges)
print_matrix(D, 0, "D0")
print_matrix(P, 0, "P0")
for k in range(num_vertices):
for i in range(num_vertices):
for j in range(num_vertices):
if D[i][k] + D[k][j] < D[i][j]:
D[i][j] = D[i][k] + D[k][j]
# Correctly update P[i][j] to reflect that the predecessor of j in the shortest i-j path is the predecessor of j in the k-j path
P[i][j] = P[k][j]
print_matrix(D, k + 1, f"D{k + 1}")
print_matrix(P, k + 1, f"P{k + 1}")
return D, P
# Define edges as (start_vertex, end_vertex, weight) for the given graph
edges = [
(0, 1, 4), (0, 5, 10), (1, 0, 3), (1, 3, 18),
(2, 1, 6), (3, 1, 5), (3, 2, 15), (3, 4, 2),
(3, 5, 19), (3, 6, 5), (4, 2, 12), (4, 3, 1),
(5, 6, 10), (6, 3, 8)
]
# Number of vertices in the graph
num_vertices = 7
final_D, final_P = floyd_warshall_with_printing(num_vertices, edges)
print("Final Distance Matrix:")
for row in final_D:
print(row)
print("\nFinal Predecessor Matrix:")
for row in final_P:
print(['None' if x is None else x for x in row]) the program is giving incorrect output of the P matrix. Final Predecessor Matrix:
['None', 0, 4, 1, 3, 0, 5]
[1, 'None', 4, 1, 3, 0, 3]
[1, 2, 'None', 1, 3, 0, 3]
[1, 3, 4, 'None', 3, 0, 3]
[1, 3, 4, 4, 'None', 0, 3]
[1, 3, 4, 6, 3, 'None', 5]
[1, 3, 4, 6, 3, 0, 'None'] in this output the intermediate nodes are not displayed correctly. Example, from v1 to v1 there is no shortest path so the P matrix should display as 0, v1 is directly connected to v2 and there is no shortest path between them so the matrix must display the entry as -(dash), from v1 to v3 there is shortest path via v2, v4, v5 and among them the highest index of the intermediate vertex on the shortest path is 5 so the matrix must display the entry as 5. The matrix P contains the highest indices of the intermediate vertices on the shortest paths as explained above. Make changes in the program accordingly
|
1de1bca8d25e48c9b0517290810f122f
|
{
"intermediate": 0.3134726285934448,
"beginner": 0.48032304644584656,
"expert": 0.20620431005954742
}
|
45,335
|
make a script to train based on this
import os
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
import re
# Define dataset class
class SpectrogramDataset(Dataset):
def __init__(self, low_quality_spectrograms, high_quality_spectrograms):
# Assuming that low_quality_spectrograms and high_quality_spectrograms are lists of Tensors
self.low_quality_spectrograms = low_quality_spectrograms
self.high_quality_spectrograms = high_quality_spectrograms
def __len__(self):
return len(self.low_quality_spectrograms)
def __getitem__(self, idx):
return self.low_quality_spectrograms[idx], self.high_quality_spectrograms[idx]
# Function to load the dataset
def load_dataset(lq_dir, hq_dir):
lq_files = os.listdir(lq_dir)
lq_specs, hq_specs = [], []
for lq_file in lq_files:
lq_path = os.path.join(lq_dir, lq_file)
hq_file = re.sub('lq', 'hq', lq_file) # Replace lq with hq in the filename
hq_path = os.path.join(hq_dir, hq_file)
if os.path.exists(hq_path): # Ensure the HQ counterpart exists
lq_spec = np.load(lq_path)
hq_spec = np.load(hq_path)
# Convert numpy arrays to tensors and append
lq_specs.append(torch.from_numpy(lq_spec).float())
hq_specs.append(torch.from_numpy(hq_spec).float())
else:
print(f'Missing HQ file for {lq_file}')
# Returning lists of tensors
return lq_specs, hq_specs
# Custom collate function to handle varying shapes within a batch
def custom_collate_fn(batch):
# Just return the list of tensors as is, no padding
lq_specs, hq_specs = zip(*batch) # Transpose the batch (list of pairs) into pairs of lists
return list(lq_specs), list(hq_specs) # In this basic form, DataLoader expects batch to be Tensor
lq_dir = "/content/dataset /processed/LQPRO" # Corrected path
hq_dir = "/content/dataset /processed/HQPRO"
# Load the dataset
lq_spectrograms, hq_spectrograms = load_dataset(lq_dir, hq_dir)
# Split and prepare the data as in the previous script
# Note: Direct splitting lists of tensors now
lq_train, lq_test, hq_train, hq_test = train_test_split(lq_spectrograms, hq_spectrograms, test_size=0.2, random_state=42)
lq_train, lq_val, hq_train, hq_val = train_test_split(lq_train, hq_train, test_size=0.25, random_state=42)
# Create datasets
train_dataset = SpectrogramDataset(lq_train, hq_train)
val_dataset = SpectrogramDataset(lq_val, hq_val)
test_dataset = SpectrogramDataset(lq_test, hq_test)
# Create dataloaders with custom collate function
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=custom_collate_fn)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False, collate_fn=custom_collate_fn)
print(f'Training set size: {len(train_dataset)}')
print(f'Validation set size: {len(val_dataset)}')
print(f'Test set size: {len(test_dataset)}')
|
f17d097d8998ee5908e59f3b47e03d56
|
{
"intermediate": 0.26865607500076294,
"beginner": 0.4814228415489197,
"expert": 0.2499210387468338
}
|
45,336
|
In Clojure, how do I get the last X values of a set?
|
8a8b357a70d77d33a46826e10e248aae
|
{
"intermediate": 0.4664136469364166,
"beginner": 0.20686401426792145,
"expert": 0.32672229409217834
}
|
45,337
|
I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. When I need to tell you something in English, I will do so by putting text inside curly brackets {like this}. My first command is pwdUser:Oc nie kapattin
Model:
|
4059d79f94b6b95e0ee8d010c1b9e890
|
{
"intermediate": 0.3036142885684967,
"beginner": 0.2719075381755829,
"expert": 0.4244782030582428
}
|
45,338
|
How can I make a fixed div in HTML not scrollable
|
bf3806e36cdde6577cee76214e1bb3c8
|
{
"intermediate": 0.40158215165138245,
"beginner": 0.3423762321472168,
"expert": 0.25604161620140076
}
|
45,339
|
def print_matrix(matrix, iteration, matrix_name):
print(f"After iteration {iteration}, {matrix_name}:“)
for row in matrix:
print([‘inf’ if x == float('inf') else x for x in row])
print()
def initialize_matrices(num_vertices, edges):
inf = float('inf')
D = [[inf if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
P = [[None for _ in range(num_vertices)] for _ in range(num_vertices)]
for start, end, weight in edges:
D[start][end] = weight
P[start][end] = start # Initializing the predecessor as the start node
return D, P
def floyd_warshall_with_printing(num_vertices, edges):
D, P = initialize_matrices(num_vertices, edges)
print_matrix(D, 0, "D0")
print_matrix(P, 0, "P0")
for k in range(num_vertices):
for i in range(num_vertices):
for j in range(num_vertices):
if D[i][k] + D[k][j] < D[i][j]:
D[i][j] = D[i][k] + D[k][j]
P[i][j] = P[k][j]
print_matrix(D, k + 1, f"D{k + 1}")
print_matrix(P, k + 1, f"P{k + 1}")
return D, P
# Assuming the numbering of vertices starts from 0. Adjust according to your input
edges = [
(0, 1, 4), (0, 5, 10), (1, 0, 3), (1, 3, 18),
(2, 1, 6), (3, 1, 5), (3, 2, 15), (3, 4, 2),
(3, 5, 19), (3, 6, 5), (4, 2, 12), (4, 3, 1),
(5, 6, 10), (6, 3, 8)
]
# Number of vertices in the graph
num_vertices = 7
final_D, final_P = floyd_warshall_with_printing(num_vertices, edges)
print("Final Distance Matrix:")
for row in final_D:
print(row)
print("\nFinal Predecessor Matrix:")
for row in final_P:
print(row)
After iteration 0, D0:
[0, 4, 'inf', 'inf', 'inf', 10, 'inf']
[3, 0, 'inf', 18, 'inf', 'inf', 'inf']
['inf', 6, 0, 'inf', 'inf', 'inf', 'inf']
['inf', 5, 15, 0, 2, 19, 5]
['inf', 'inf', 12, 1, 0, 'inf', 'inf']
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
['inf', 'inf', 'inf', 8, 'inf', 'inf', 0]
After iteration 0, P0:
[None, 0, None, None, None, 0, None]
[1, None, None, 1, None, None, None]
[None, 2, None, None, None, None, None]
[None, 3, 3, None, 3, 3, 3]
[None, None, 4, 4, None, None, None]
[None, None, None, None, None, None, 5]
[None, None, None, 6, None, None, None]
After iteration 1, D1:
[0, 4, 'inf', 'inf', 'inf', 10, 'inf']
[3, 0, 'inf', 18, 'inf', 13, 'inf']
['inf', 6, 0, 'inf', 'inf', 'inf', 'inf']
['inf', 5, 15, 0, 2, 19, 5]
['inf', 'inf', 12, 1, 0, 'inf', 'inf']
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
['inf', 'inf', 'inf', 8, 'inf', 'inf', 0]
After iteration 1, P1:
[None, 0, None, None, None, 0, None]
[1, None, None, 1, None, 0, None]
[None, 2, None, None, None, None, None]
[None, 3, 3, None, 3, 3, 3]
[None, None, 4, 4, None, None, None]
[None, None, None, None, None, None, 5]
[None, None, None, 6, None, None, None]
After iteration 2, D2:
[0, 4, 'inf', 22, 'inf', 10, 'inf']
[3, 0, 'inf', 18, 'inf', 13, 'inf']
[9, 6, 0, 24, 'inf', 19, 'inf']
[8, 5, 15, 0, 2, 18, 5]
['inf', 'inf', 12, 1, 0, 'inf', 'inf']
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
['inf', 'inf', 'inf', 8, 'inf', 'inf', 0]
After iteration 2, P2:
[None, 0, None, 1, None, 0, None]
[1, None, None, 1, None, 0, None]
[1, 2, None, 1, None, 0, None]
[1, 3, 3, None, 3, 0, 3]
[None, None, 4, 4, None, None, None]
[None, None, None, None, None, None, 5]
[None, None, None, 6, None, None, None]
After iteration 3, D3:
[0, 4, 'inf', 22, 'inf', 10, 'inf']
[3, 0, 'inf', 18, 'inf', 13, 'inf']
[9, 6, 0, 24, 'inf', 19, 'inf']
[8, 5, 15, 0, 2, 18, 5]
[21, 18, 12, 1, 0, 31, 'inf']
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
['inf', 'inf', 'inf', 8, 'inf', 'inf', 0]
After iteration 3, P3:
[None, 0, None, 1, None, 0, None]
[1, None, None, 1, None, 0, None]
[1, 2, None, 1, None, 0, None]
[1, 3, 3, None, 3, 0, 3]
[1, 2, 4, 4, None, 0, None]
[None, None, None, None, None, None, 5]
[None, None, None, 6, None, None, None]
After iteration 4, D4:
[0, 4, 37, 22, 24, 10, 27]
[3, 0, 33, 18, 20, 13, 23]
[9, 6, 0, 24, 26, 19, 29]
[8, 5, 15, 0, 2, 18, 5]
[9, 6, 12, 1, 0, 19, 6]
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
[16, 13, 23, 8, 10, 26, 0]
After iteration 4, P4:
[None, 0, 3, 1, 3, 0, 3]
[1, None, 3, 1, 3, 0, 3]
[1, 2, None, 1, 3, 0, 3]
[1, 3, 3, None, 3, 0, 3]
[1, 3, 4, 4, None, 0, 3]
[None, None, None, None, None, None, 5]
[1, 3, 3, 6, 3, 0, None]
After iteration 5, D5:
[0, 4, 36, 22, 24, 10, 27]
[3, 0, 32, 18, 20, 13, 23]
[9, 6, 0, 24, 26, 19, 29]
[8, 5, 14, 0, 2, 18, 5]
[9, 6, 12, 1, 0, 19, 6]
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
[16, 13, 22, 8, 10, 26, 0]
After iteration 5, P5:
[None, 0, 4, 1, 3, 0, 3]
[1, None, 4, 1, 3, 0, 3]
[1, 2, None, 1, 3, 0, 3]
[1, 3, 4, None, 3, 0, 3]
[1, 3, 4, 4, None, 0, 3]
[None, None, None, None, None, None, 5]
[1, 3, 4, 6, 3, 0, None]
After iteration 6, D6:
[0, 4, 36, 22, 24, 10, 20]
[3, 0, 32, 18, 20, 13, 23]
[9, 6, 0, 24, 26, 19, 29]
[8, 5, 14, 0, 2, 18, 5]
[9, 6, 12, 1, 0, 19, 6]
['inf', 'inf', 'inf', 'inf', 'inf', 0, 10]
[16, 13, 22, 8, 10, 26, 0]
After iteration 6, P6:
[None, 0, 4, 1, 3, 0, 5]
[1, None, 4, 1, 3, 0, 3]
[1, 2, None, 1, 3, 0, 3]
[1, 3, 4, None, 3, 0, 3]
[1, 3, 4, 4, None, 0, 3]
[None, None, None, None, None, None, 5]
[1, 3, 4, 6, 3, 0, None]
After iteration 7, D7:
[0, 4, 36, 22, 24, 10, 20]
[3, 0, 32, 18, 20, 13, 23]
[9, 6, 0, 24, 26, 19, 29]
[8, 5, 14, 0, 2, 18, 5]
[9, 6, 12, 1, 0, 19, 6]
[26, 23, 32, 18, 20, 0, 10]
[16, 13, 22, 8, 10, 26, 0]
After iteration 7, P7:
[None, 0, 4, 1, 3, 0, 5]
[1, None, 4, 1, 3, 0, 3]
[1, 2, None, 1, 3, 0, 3]
[1, 3, 4, None, 3, 0, 3]
[1, 3, 4, 4, None, 0, 3]
[1, 3, 4, 6, 3, None, 5]
[1, 3, 4, 6, 3, 0, None]
Final Distance Matrix:
[0, 4, 36, 22, 24, 10, 20]
[3, 0, 32, 18, 20, 13, 23]
[9, 6, 0, 24, 26, 19, 29]
[8, 5, 14, 0, 2, 18, 5]
[9, 6, 12, 1, 0, 19, 6]
[26, 23, 32, 18, 20, 0, 10]
[16, 13, 22, 8, 10, 26, 0]
Final Predecessor Matrix:
[None, 0, 4, 1, 3, 0, 5]
[1, None, 4, 1, 3, 0, 3]
[1, 2, None, 1, 3, 0, 3]
[1, 3, 4, None, 3, 0, 3]
[1, 3, 4, 4, None, 0, 3]
[1, 3, 4, 6, 3, None, 5]
[1, 3, 4, 6, 3, 0, None]
=== Code Execution Successful === I want you to add 1 to each entry in the matrix P at every iteration to get the correct output.
|
7425b7955aba7a76e53ac727766dba08
|
{
"intermediate": 0.2896045446395874,
"beginner": 0.5324080586433411,
"expert": 0.17798744142055511
}
|
45,340
|
def print_matrix(matrix, iteration, matrix_name):
print(f"After iteration {iteration}, {matrix_name}:")
for row in matrix:
formatted_row = ['inf' if x == float('inf') else f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row)))
print()
def initialize_matrices(num_vertices, edges):
inf = float('inf')
D = [[inf if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
P = [[0 if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
for start, end, weight in edges:
D[start][end] = weight
P[start][end] = start + 1
return D, P
def floyd_warshall_with_printing(num_vertices, edges):
D, P = initialize_matrices(num_vertices, edges)
print_matrix(D, 0, "D0")
print_matrix(P, 0, "P0")
for k in range(num_vertices):
for i in range(num_vertices):
for j in range(num_vertices):
if D[i][k] + D[k][j] < D[i][j]:
D[i][j] = D[i][k] + D[k][j]
P[i][j] = P[k][j]
print_matrix(D, k + 1, f"D{k + 1}")
print_matrix(P, k + 1, f"P{k + 1}")
return D, P
edges = [
(0, 1, 4), (0, 5, 10), (1, 0, 3), (1, 3, 18),
(2, 1, 6), (3, 1, 5), (3, 2, 15), (3, 4, 2),
(3, 5, 19), (3, 6, 5), (4, 2, 12), (4, 3, 1),
(5, 6, 10), (6, 3, 8)
]
num_vertices = 7
final_D, final_P = floyd_warshall_with_printing(num_vertices, edges)
print("Final Distance Matrix:")
for row in final_D:
formatted_row = ['inf' if x == float('inf') else f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row)))
print("\nFinal Predecessor Matrix:")
for row in final_P:
formatted_row = [f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row))) i want the program to print ∞ instead of inf in the matrix
|
0587b3382d0dacc71c8ed60ebcc454b4
|
{
"intermediate": 0.3221440315246582,
"beginner": 0.5277853608131409,
"expert": 0.15007062256336212
}
|
45,341
|
Cite all the Functional units of a GPU inside a table and indicate each one's rule
|
cc541dd8b5c37015f1f130fedca36b8a
|
{
"intermediate": 0.15386034548282623,
"beginner": 0.3772597014904022,
"expert": 0.46887993812561035
}
|
45,342
|
hello
|
692696f9e27cc0cea3833c96f26b56a5
|
{
"intermediate": 0.32064199447631836,
"beginner": 0.28176039457321167,
"expert": 0.39759764075279236
}
|
45,343
|
Assuming you are an experienced data engineer, can you create a data model (star schema) of ride share related products?
|
8ced83d24880840a0157cd68831e3b6e
|
{
"intermediate": 0.27603787183761597,
"beginner": 0.22582778334617615,
"expert": 0.4981343746185303
}
|
45,344
|
How can I create a interceptor for angular v17
|
2b47ca7b51f8b9e825459cb785a45a61
|
{
"intermediate": 0.38652172684669495,
"beginner": 0.11249583959579468,
"expert": 0.500982403755188
}
|
45,345
|
def print_matrix(matrix, iteration, matrix_name):
print(f"After iteration {iteration}, {matrix_name}:")
for row in matrix:
formatted_row = [' ∞' if x == float('inf') else f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row)))
print()
def initialize_matrices(num_vertices, edges):
inf = float('inf')
D = [[inf if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
P = [[0 if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
for start, end, weight in edges:
D[start][end] = weight
P[start][end] = start + 1
return D, P
def floyd_warshall_with_printing(num_vertices, edges):
D, P = initialize_matrices(num_vertices, edges)
print_matrix(D, 0, "D0")
print_matrix(P, 0, "P0")
for k in range(num_vertices):
for i in range(num_vertices):
for j in range(num_vertices):
if D[i][k] + D[k][j] < D[i][j]:
D[i][j] = D[i][k] + D[k][j]
P[i][j] = P[k][j]
print_matrix(D, k + 1, f"D{k + 1}")
print_matrix(P, k + 1, f"P{k + 1}")
return D, P
edges = [
(0, 1, 4), (0, 5, 10), (1, 0, 3), (1, 3, 18),
(2, 1, 6), (3, 1, 5), (3, 2, 15), (3, 4, 2),
(3, 5, 19), (3, 6, 5), (4, 2, 12), (4, 3, 1),
(5, 6, 10), (6, 3, 8)
]
num_vertices = 7
final_D, final_P = floyd_warshall_with_printing(num_vertices, edges)
print("Final Distance Matrix:")
for row in final_D:
formatted_row = [' ∞' if x == float('inf') else f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row)))
print("\nFinal Predecessor Matrix:")
for row in final_P:
formatted_row = [f'{x:3}' for x in row]
print('[{}]'.format(', '.join(formatted_row)))
After iteration 0, D0:
[ 0, 4, ∞, ∞, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, ∞, ∞]
[ ∞, 6, 0, ∞, ∞, ∞, ∞]
[ ∞, 5, 15, 0, 2, 19, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 0, P0:
[ 0, 1, 0, 0, 0, 1, 0]
[ 2, 0, 0, 2, 0, 0, 0]
[ 0, 3, 0, 0, 0, 0, 0]
[ 0, 4, 4, 0, 4, 4, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 1, D1:
[ 0, 4, ∞, ∞, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ ∞, 6, 0, ∞, ∞, ∞, ∞]
[ ∞, 5, 15, 0, 2, 19, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 1, P1:
[ 0, 1, 0, 0, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 0, 3, 0, 0, 0, 0, 0]
[ 0, 4, 4, 0, 4, 4, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 2, D2:
[ 0, 4, ∞, 22, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ 9, 6, 0, 24, ∞, 19, ∞]
[ 8, 5, 15, 0, 2, 18, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 2, P2:
[ 0, 1, 0, 2, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 2, 3, 0, 2, 0, 1, 0]
[ 2, 4, 4, 0, 4, 1, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 3, D3:
[ 0, 4, ∞, 22, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ 9, 6, 0, 24, ∞, 19, ∞]
[ 8, 5, 15, 0, 2, 18, 5]
[ 21, 18, 12, 1, 0, 31, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 3, P3:
[ 0, 1, 0, 2, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 2, 3, 0, 2, 0, 1, 0]
[ 2, 4, 4, 0, 4, 1, 4]
[ 2, 3, 5, 5, 0, 1, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 4, D4:
[ 0, 4, 37, 22, 24, 10, 27]
[ 3, 0, 33, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 15, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 23, 8, 10, 26, 0]
After iteration 4, P4:
[ 0, 1, 4, 2, 4, 1, 4]
[ 2, 0, 4, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 4, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 4, 7, 4, 1, 0]
After iteration 5, D5:
[ 0, 4, 36, 22, 24, 10, 27]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 5, P5:
[ 0, 1, 5, 2, 4, 1, 4]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
After iteration 6, D6:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 6, P6:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
After iteration 7, D7:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ 26, 23, 32, 18, 20, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 7, P7:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 2, 4, 5, 7, 4, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
Final Distance Matrix:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ 26, 23, 32, 18, 20, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
Final Predecessor Matrix:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 2, 4, 5, 7, 4, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
=== Code Execution Successful === i want the program to print '-' in the Predecessor matrix where there is a direct link between the 2 vertices
|
7aa24854c18672e074c90ec4117bbba5
|
{
"intermediate": 0.2970334589481354,
"beginner": 0.4500461518764496,
"expert": 0.25292035937309265
}
|
45,346
|
def print_matrix(matrix, iteration, matrix_name):
print(f"After iteration {iteration}, {matrix_name}:“)
for row in matrix:
formatted_row = [’ ∞’ if x == float(‘inf’) else f’{x:3}’ for x in row]
print(‘[{}]’.format(', '.join(formatted_row)))
print()
def initialize_matrices(num_vertices, edges):
inf = float(‘inf’)
D = [[inf if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
P = [[0 if i != j else 0 for i in range(num_vertices)] for j in range(num_vertices)]
for start, end, weight in edges:
D[start][end] = weight
P[start][end] = start + 1
return D, P
def floyd_warshall_with_printing(num_vertices, edges):
D, P = initialize_matrices(num_vertices, edges)
print_matrix(D, 0, “D0”)
print_matrix(P, 0, “P0”)
for k in range(num_vertices):
for i in range(num_vertices):
for j in range(num_vertices):
if D[i][k] + D[k][j] < D[i][j]:
D[i][j] = D[i][k] + D[k][j]
P[i][j] = P[k][j]
print_matrix(D, k + 1, f"D{k + 1}”)
print_matrix(P, k + 1, f"P{k + 1}“)
return D, P
edges = [
(0, 1, 4), (0, 5, 10), (1, 0, 3), (1, 3, 18),
(2, 1, 6), (3, 1, 5), (3, 2, 15), (3, 4, 2),
(3, 5, 19), (3, 6, 5), (4, 2, 12), (4, 3, 1),
(5, 6, 10), (6, 3, 8)
]
num_vertices = 7
final_D, final_P = floyd_warshall_with_printing(num_vertices, edges)
print(“Final Distance Matrix:”)
for row in final_D:
formatted_row = [’ ∞’ if x == float(‘inf’) else f’{x:3}’ for x in row]
print(‘[{}]’.format(', '.join(formatted_row)))
print(”\nFinal Predecessor Matrix:")
for row in final_P:
formatted_row = [f’{x:3}’ for x in row]
print(‘[{}]’.format(', '.join(formatted_row)))
After iteration 0, D0:
[ 0, 4, ∞, ∞, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, ∞, ∞]
[ ∞, 6, 0, ∞, ∞, ∞, ∞]
[ ∞, 5, 15, 0, 2, 19, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 0, P0:
[ 0, 1, 0, 0, 0, 1, 0]
[ 2, 0, 0, 2, 0, 0, 0]
[ 0, 3, 0, 0, 0, 0, 0]
[ 0, 4, 4, 0, 4, 4, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 1, D1:
[ 0, 4, ∞, ∞, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ ∞, 6, 0, ∞, ∞, ∞, ∞]
[ ∞, 5, 15, 0, 2, 19, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 1, P1:
[ 0, 1, 0, 0, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 0, 3, 0, 0, 0, 0, 0]
[ 0, 4, 4, 0, 4, 4, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 2, D2:
[ 0, 4, ∞, 22, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ 9, 6, 0, 24, ∞, 19, ∞]
[ 8, 5, 15, 0, 2, 18, 5]
[ ∞, ∞, 12, 1, 0, ∞, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 2, P2:
[ 0, 1, 0, 2, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 2, 3, 0, 2, 0, 1, 0]
[ 2, 4, 4, 0, 4, 1, 4]
[ 0, 0, 5, 5, 0, 0, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 3, D3:
[ 0, 4, ∞, 22, ∞, 10, ∞]
[ 3, 0, ∞, 18, ∞, 13, ∞]
[ 9, 6, 0, 24, ∞, 19, ∞]
[ 8, 5, 15, 0, 2, 18, 5]
[ 21, 18, 12, 1, 0, 31, ∞]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ ∞, ∞, ∞, 8, ∞, ∞, 0]
After iteration 3, P3:
[ 0, 1, 0, 2, 0, 1, 0]
[ 2, 0, 0, 2, 0, 1, 0]
[ 2, 3, 0, 2, 0, 1, 0]
[ 2, 4, 4, 0, 4, 1, 4]
[ 2, 3, 5, 5, 0, 1, 0]
[ 0, 0, 0, 0, 0, 0, 6]
[ 0, 0, 0, 7, 0, 0, 0]
After iteration 4, D4:
[ 0, 4, 37, 22, 24, 10, 27]
[ 3, 0, 33, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 15, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 23, 8, 10, 26, 0]
After iteration 4, P4:
[ 0, 1, 4, 2, 4, 1, 4]
[ 2, 0, 4, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 4, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 4, 7, 4, 1, 0]
After iteration 5, D5:
[ 0, 4, 36, 22, 24, 10, 27]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 5, P5:
[ 0, 1, 5, 2, 4, 1, 4]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
After iteration 6, D6:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ ∞, ∞, ∞, ∞, ∞, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 6, P6:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 0, 0, 0, 0, 0, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
After iteration 7, D7:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ 26, 23, 32, 18, 20, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
After iteration 7, P7:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 2, 4, 5, 7, 4, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
Final Distance Matrix:
[ 0, 4, 36, 22, 24, 10, 20]
[ 3, 0, 32, 18, 20, 13, 23]
[ 9, 6, 0, 24, 26, 19, 29]
[ 8, 5, 14, 0, 2, 18, 5]
[ 9, 6, 12, 1, 0, 19, 6]
[ 26, 23, 32, 18, 20, 0, 10]
[ 16, 13, 22, 8, 10, 26, 0]
Final Predecessor Matrix:
[ 0, 1, 5, 2, 4, 1, 6]
[ 2, 0, 5, 2, 4, 1, 4]
[ 2, 3, 0, 2, 4, 1, 4]
[ 2, 4, 5, 0, 4, 1, 4]
[ 2, 4, 5, 5, 0, 1, 4]
[ 2, 4, 5, 7, 4, 0, 6]
[ 2, 4, 5, 7, 4, 1, 0]
=== Code Execution Successful === i want the program to print ‘-’ in the Predecessor matrix where there is a directed edge between the 2 vertices
|
fd41ec3971229648b1a90b29a970cfd5
|
{
"intermediate": 0.3046589195728302,
"beginner": 0.5334779620170593,
"expert": 0.16186314821243286
}
|
45,347
|
Use Floyd's algorithm to find all pair shortest paths in the following graph:
The graph has 7 vertices namely v1, v2, v3, v4, v5, v6, v7. v1 has an outgoing edge to v2 at a distance of 4 and outgoing edge to v6 at a distance of 10. v2 has an outgoing edge to v1 at a distance of 3 and outgoing edge to v4 at a distance of 18. v3 has an outgoing edge to v2 at a distance of 6. v4 has an outgoing edge to v2 at a distance of 5, outgoing edge to 3 at a distance of 15, outgoing edge to v5 at a distance of 2, outgoing edge to v6 at a distance of 19 and outgoing edge to v7 at a distance of 5. v5 has an outgoing edge to v3 at a distance of 12 and outgoing edge to v4 at a distance of 1. v6 has an outgoing edge to v7 at a distance of 10. v7 has an outgoing edge to v4 at a distance of 8.
Use the Print Shortest Path algorithm ( path(index q, r)
if (P[ q, r ]!=0)
path(q, P[q, r])
println( “v”+ P[q, r])
path(P[q, r], r)
return;
//no intermediate nodes
else return
• If D[q, r] < ∞, print node q and call path(...)
• After returning from path(...), print node r ) to find the shortest path from vertex v7 to vertex v3 using the matrix P = [ 0, 0, 5, 2, 4, 0, 6]
[ 0, 0, 5, 0, 4, 1, 4]
[ 2, 0, 0, 2, 4, 1, 4]
[ 2, 0, 5, 0, 0, 1, 0]
[ 2, 4, 0, 0, 0, 1, 4]
[ 2, 4, 5, 7, 4, 0, 0]
[ 2, 4, 5, 0, 4, 1, 0]. Write the algorithm for it.
|
ab501df86a7b8b13ed85594dbc6b2611
|
{
"intermediate": 0.26607173681259155,
"beginner": 0.16855491697788239,
"expert": 0.5653733015060425
}
|
45,348
|
Use Floyd’s algorithm to find all pair shortest paths in the following graph:
The graph has 7 vertices namely v1, v2, v3, v4, v5, v6, v7. v1 has an outgoing edge to v2 at a distance of 4 and outgoing edge to v6 at a distance of 10. v2 has an outgoing edge to v1 at a distance of 3 and outgoing edge to v4 at a distance of 18. v3 has an outgoing edge to v2 at a distance of 6. v4 has an outgoing edge to v2 at a distance of 5, outgoing edge to 3 at a distance of 15, outgoing edge to v5 at a distance of 2, outgoing edge to v6 at a distance of 19 and outgoing edge to v7 at a distance of 5. v5 has an outgoing edge to v3 at a distance of 12 and outgoing edge to v4 at a distance of 1. v6 has an outgoing edge to v7 at a distance of 10. v7 has an outgoing edge to v4 at a distance of 8.
Analyze the Print Shortest Path algorithm ( path(index q, r)
if (P[ q, r ]!=0)
path(q, P[q, r])
println( “v”+ P[q, r])
path(P[q, r], r)
return;
//no intermediate nodes
else return
• If D[q, r] < ∞, print node q and call path(…)
• After returning from path(…), print node r ) and show that it has a linear-time complexity (input size is the number of vertices in the graph).
(Hint: You can consider each array access to P[i][j] as a basic operation.)
|
a00e495dd24deff982000eec55e1e65f
|
{
"intermediate": 0.2391333431005478,
"beginner": 0.1863425076007843,
"expert": 0.5745241641998291
}
|
45,349
|
Hi, I'm making a website with ClojureScript, Reframe, reagent, and Apex Charts.
(defn cost-summary-chart [job-list]
(let [remaining-cost #(- (get-estimated-costs %) (get-actual-costs %))]
[ApexChart {:options (clj->js
{:chart {:id "cost-summary-chart"}
:labels ["Incurred Cost" "Remaining Cost"]
:legend {:position "bottom"}
:plotOptions {:pie
{:donut {:labels
{:show true
:total {:show true
:label "Total Cost"
:formatter (fn [val]
(str "Total is " val (get-estimated-costs job-list)))}}}}}
:title {:text "Cost Summary"
:align "left"}
:colors (colours :teal-dark :orange)})
:series [(get-actual-costs job-list) (remaining-cost job-list)]
:type "donut"
:width 500
:height 400}]))
The value in formatter doesn't work properly. I've tried val and the function to get estimated costs, but val returns object Object and get-estimated-costs returns the correct value but doesn't update
|
5cd910fdeff34352860135c5b15affea
|
{
"intermediate": 0.3875311017036438,
"beginner": 0.37284696102142334,
"expert": 0.23962190747261047
}
|
45,350
|
the right sequence of prompts combined with your unique domain knowledge is an entire five six or seven figure product in itself in this video I want to dig into seven prompt chains with real examples you can use to level up your prompt engineering ability to build better products tools and applications the name of the game in software now is how can you build a gentic software that creates value on your behalf for you and your users I've got seven prompt chains I want to show you that can help you achieve that goal let's start with the snowball prompt chain we're using CLA 3 Haiku Sonet and Opus models and we're passing one or more of our models into our prompt chain examples the snowball is a really interesting prompt that allows you to start with a little information that is slowly developed over each prompt let's look at the mermaid chart to see exactly how this works so you start with Base information then you run one to n prompts that gather and create information based on your original information with each prompt the context improves and then finally you run a summary or format prompt that finalizes this AI agent's run so let's look at a real example here right I have this Base information three unusual use cases for llms I'm passing it into this prompt where I want to generate click worthy titles about this topic and that's our Base information so from this single prompt we're having our model respond in Json format where it's going to return a click-worthy title and our topic and that's going to create our first snowball after we have that we ask you to generate a compelling three section outline given the information and you can see here we then repeat the pattern right now we have the title the topic and the sections that creates our second snowball of information you can see where this is going right you can basically just continue to do this and add arbitrary information over time while keeping each prompt specialized to generate one thing it's really important that you treat all of your llms like individual functions with concrete inputs and outputs that allows you to fine-tune and tailor each prompt to solve its problem the best it possibly can so let's run our snowball prompt and let's see what we get so you can see we have our first snowball second snowball and then finally we're getting content and then lastly we put it all together in a combined markdown blog as you can see at each step the llm is adding information it's literally enlarging the context of the problem that we're trying to solve at first we only have a topic we pass that in and the first no ball is created giving us a topic and a title then it creates sections then it creates content and then finally we put it all together into a final cohesive markdown blog and you can see here we're writing that all to a file we can go ahead and take a look at this file here and the best part is of course it's completely reusable this is a really powerful interesting chain that's really great for writing blogs building newsletters doing some research writing summaries and just a note on the terminology here prompt chaining prompt orchestration prompt graphs they're all referring to the same idea of generating concrete outputs by combining llms models and code together to drive outcomes so this is one way you can drive a specific set of outcomes let's move on to our next prompt chain this is the worker pattern so you've definitely seen this before it's a really popular pattern in research tools so it's likely you've heard of GPT researcher or something similar the worker pattern is one that is really popular when it comes to doing research putting together ideas making web requests and then doing research based on the response of the request you can see here GPT researcher is doing something completely similar they have a task they generate questions and then they fan out into individual prompts that then get combined into a final report this is the most popular pattern for research type tasks so let's go ahead and dive into this I like to call this the worker pattern where you basically delegate parts of your workload to individual prompts all this code by the way is going to be in a really simplified Guist there are a couple requirements I'll put it all in the Guist you'll be able to fire this up in no time link for that is going to be in the description here is what the worker pattern looks like so have your initial planning prompt then it creates end steps to be processed or nend items to be processed for each one of your workers your workers then individually and ideally in parallel run their prompts and then they all funnel their results into a final summary or format prompt this creates really really powerful agents that are able to gather information on the Fly based on your initial planning prompt do research generate content generate code generate whatever you're looking for and then pull it all together into a fin finalized output this is one of the most popular prompt chaining patterns prompt orchestration patterns that's getting used right now as the use cases are very obvious let's go ahead and take a look at an interesting example so first off we'll just go ahead and run this so while this is running let's go ahead and walk through the code just like you saw we have this code planner prompt all we're asking here is generate function stubs with detailed comments on how to write the code to build a couple of functions here we're then giving an example of what exactly we want and then we're saying respond in this Json format where we have a list of function definitions but not the full function right this is our planning step after we have that we then load out our function stubs from our Json response I highly recommend that you just default to using Json responses it creates a more simplified consistent structure for all of your prompt chains we then Loop through each one of the function stubs and as you can guess we're then running prompts for each one of those function stubs and then combining it into a single large string after we have that large string with all of the results just like as you saw in our diagram here all of these results from each worker prompt get funneled in to our last summary SL format prompt where we then clean up the code and combine it into a single final python file that can be executed we'll specify one more Json format and then we just dump all that to files. py so you can see here we're actually using this pattern to build out entire file modules so we're essentially using the worker pattern as a kind of micro AI coding assistant that can build out entire files for us with related functionality right in this example we're building out a file writing module that allows us to you know write Json files write yamal files and write Tomo files all right so let's see how our prompt has done all right awesome so it's finished here let's go ahead and take a look at that file that's generated so we now should have a files. py let's go ahead and look at that and okay so we have a little bit of extra here that's fine let's go ahead and just remove these lines and Bam so you can see here we have let's go ahead and collapse everything here here so you can see here we have three really clean functions written out with comments and examples of exactly how to use it this looks really good and I hope this shows off the power of using your prompts to do one thing and do one thing well right we had our planner just plan the function sub and then each worker actually wrote out each individual function right so you can be as detailed about how you want to write a function as possible and then you just Loop that over however many of those functions you actually want to write based on your plan prompt so really allows you to divide and conquer in the truest sense and really keep all of your prompts isolated and then of course the summary format we can clean that up a little bit if we contrl Z we had a little bit of extra here this can all be cleaned up as you know with proper management of the prompt and the llm let's look at a more unique prompt this is one that I built into a product that I am actively building right now let me show off the fallback prompt chain if you're enjoying learning about these prompt chains or refreshing your memory on these prompt chains CH definitely hit the sub hit the like helps out the channel I think we're hitting 10K Subs literally as we're filming this video huge thanks for everyone watching let's keep moving the fallback prompt chain is really interesting it allows you to run a prompt an llm an entire model and if something goes wrong with the process that runs after the prompt it then falls back onto another prompt or model let me show you exactly what this looks like so you can see here we start out with our initial top priority prompt or model this pattern allows you to do something like this you can run your fastest cheapest model first you take the response of the prompt and you run whatever code or whatever process you have that you wanted your prompt to generate for you if your process fails you then run your secondary or your fallback model you then run your process again if it fails again then you use your Last Resort final big chunky expensive model but if at any point before that your cheaper faster model runs and succeeds this AI agent this prompt flow is just complete that's the big Advantage here it allows you to run your cheapest fastest prompt and model first let me show you a concrete example so I buildt up this little function fallback flow functionality I'm not going to dig into the code too much but I just want to focus on the highle flow here for you here we're generating code we're saying generate the solution in Python given this function definition so we're just giving it a function definition so we're saying text to speech passing in text and then we want to get bytes back we're asking for the response in Json format and then look at what we're doing here we have a list of functions where the first parameter of the Tuple is going to be a function call to each Cloud 3 Model so you can see here we're starting with our ha coup cheap fast top priority model we then use our cheap moderate secondary fallback model and then at the very end if all fails then we use the Opus model though key with the fallback flow prompt chain prompt graph prompt orchestration flow is that you need an evaluator function right and your evaluator is basically what are you trying to run given the output of your llm right given the output of each one of your fallback functions and in this case to validate our output we can just run the code right and in the actuality this doesn't actually do anything I'm just running this coin flip function here that's going to 50/50% chance return true or false but you can imagine that you're actually running the code and then if it's correct you then proceed with your application but if it's wrong that's when the fallback function kicks in so let me go ahead and just run this and show you a couple examples of what this looks like so here's a fallback flow so we've Fallen back to Sonet we' Fallen back again and now Opus is running there we go so our Opus model was the final winner here it looks like the code that generated is using some okay it's using Google's text to speech module that's cool we don't really care about that it's all about the prompt chain so let's go ahead and run that again right since this is a 50/50 random coin flip we're going to be successful some of the times with our earlier prompt and fail in other cases so let's go ahead and run that again bam okay so you can see here in this example you know if your first top prior fast cheap model worked your flow is finished right there's no reason to fall back so this is a prompt chaining framework that I built into an application called talk to your database this is a text to SQL to results application buil to help you retrieve information from your SQL databases faster than ever but you can see that pattern concretely used in the agent customization if our caches Miss we'll then fall back on these customizable agents that generate your SQL for you based on your natural language query and you can see here we first run grock mixt because it's hyper hyper hyper fast but if this fails what we're going to do here is actually fall back to gpg 3.5 right so little higher accuracy still got a lot of great speed still really cheap but if that still fails say you're running a really complex query it just gets the SQL statement wrong it'll then just fall back to a big beefy gbg4 SQL agent I've got it on the road map to add the CLA Opus model that's probably going to be an even bigger fallback than gp4 given its benchmarks I just wanted to show this off because this is a productionize example of how you can utilize the fallback flow inside the application you can see this working in practice so if I just run a random natural language query here we'll say let we open up the tables we'll say products price less than 50 you can see this is going to return basically right away based on the Simplicity of it and based on all the benchmarks I've run I can almost guarantee you that this was the result of the grock mixt dral model right so I just wanted to show that off in a real productionize concrete example feel free to check out talk to your database I'll leave the link to the app in the description the app really only has one purpose and it's to help you retrieve information faster than ever from your database so that's a concrete example of how you can use fallback flow the big win here is that it allows you to save money and save time but you also increase the reliability and the accuracy of your AI agent as a whole because if something doesn't work it'll just fall back to the next available model and the next available prompt and the prompt is also another dimension of this prompt chain that you can tweet maybe you'll have a longer more detailed prompt and a more powerful model in your second or third execution of your fallback function so this is another really powerful pattern that you can add to your agentic systems let's go ahead and keep moving let's talk about the decision maker prompt chain this is a fairly simple one we've done videos on a couple of these prompt chains in the past we'll go ahead and kick this one off so the decision prompt chain works like this it's really simple you ask your llm to decide something for you and based on those results you run different prompt chains you run different code flows let's look at a really simple example of how you can use the decision prompt chain so it's really great for Creative Direction dictating flow control making decisions you can see here we have a list of statements that you might find in a quarterly report from a company things like our new product launch has been well received by customers and is contributing significantly to our growth and then other negative type things like the competitive landscape remains challenging with some competitors engaging in aggressive pressing strategies right so imagine you have a live feed of these statements coming in and you're analyzing it and and what you want your decision-making agent to do that's listening to this live feed you wanted to analyze positive versus negative sentiment this is a really popular use case of making decisions on the Fly analyzing sentiment to make decisions on your behalf this is a really powerful way a powerful technique a powerful prompt chain to utilize in your products the sentiment analysis then responds either positive or negative and then what you can do essentially is map the result to an action right so you can see here in this simple map we have positive mapped to a function and negative mapped to a function and then we have an unknown fallback method right and then you just call whatever your next step is right so This Is Us running you know the prompt chain one prompt chain two prompt chain 3 whatever the next step is here that's what this function map represents and in this case we're just saying you know the following text has a positive sentiment generate a short thesis about why this is true really you could do anything inside your next step your next action that's really up to you and whatever domain or feature that you're working through the power in this lies in being able to offload decision-making into your AI agents so you can see here we analyze the sentiment here we incurred higher than expected costs this is of course is going to come through as negative negative sentiment thesis and then it's just giving a brief explanation the core value proposition here is to remember that based on the decision that your llm has made you can now run arbitrary code arbitrary additional prompts and this is where a lot of latent value of llms really exists so let's move on let's talk about plan and execute so this is one that you're likely to be familiar with we don't have to go into this in too much detail but this is your typical Chain of Thought tree of thought any type of planning first then execute sequence of prompts will essentially get you to this result let's look at the diagram for this in its Essence it's really simple you start you plan then you execute based on your plan and then you end we saw a more concrete example of this in the worker prompt chain but in its simplified form it really only needs two prompts to run first you do your planning then you do your execution and just as a simple example here we have a simple task we're going to be designing software architecture for an AI assistant that uses text of speech llms and a local sqi database we then prompt our agent to you know make a plan we have this classic activation phrase let's think step by step there are several variants of this you can find all over online but they all boil down to the same thing let your llm think first give it time to think and in that thinking it acts as a context Builder context formatter kind of a memory arranger for your next prompt where you actually do the thing that you would have prompted originally in one shot so let's go ahead and run this excellent so you can see we have use cases we have diagrams we have components we have an overview that's all running nice and clean and then we have our output at the end so the idea here of course is without the plan the final output would not be as good so I'll let you play with that we don't need to dig into that one too much that's a really popular prompt chain just as this next one is so let's talk about human in the loop this is a simple one basically it's any UI ux combination where you're running a prompt and then on some Loop or via some UI you are asking for user input right that's essentially what this pattern is and we can visualize this with this mermaid chart where we have our initial prompt we then ask explicitly for feedback we run our iterative prompt and then give our llm more feedback and this runs in a loop until we get the result we're looking for and then things finally end so I'm not going to run this it's pretty straightforward you run your initial prompt so here we're saying generate five ideas surrounding this topic and then while true iterate on this idea unless we type done and this just lets you build up context build up a conversation build up concrete results over over it allows you to go back and forth this brings us to a really really important point about prompt chaining and Building Products if you think about it this single flow prompt feedback iterative feedback that flow is exactly what the chat GPT application is right you're typing a message this is your base prompt it responds to you and then you're saying something else right you're giving it some feedback you're having a conversation you're going back and forth so it seems obvious to say it out loud but I just want to highlight that this single PR flow is an entire product and it's like yeah of course it is but but I think it really highlights an interesting idea that we haven't really seen or have have truly explored the full capabilities of llms by any stretch of the imagination right there have been so many products coming out that is just this it's just the chat interface this is something I mentioned in the 2024 predictions video um we are going to get so sick and tired of the chat interface and at some point someone's going to innovate on it and create something more interesting there are definitely variants of this for instance in talk to your database there is a prompt results type of format right so we're not having an ongoing conversation here in talk to database you're just writing a natural language query right you're saying you know jobs id5 and you have a bunch of Agents writing in the background that just give you the result you're looking for right so this is more like a call response type of prompt framework and as I mentioned behind the scenes we're using the fallback prompt chain but I just want to highlight that idea that there are so many applications being built with the chat interface and under the hood that's just one prompt chain so there's so much Innovation there's so much to build there's so much to create I hope that this makes sense and I hope that you can see you know all the potential value that every one of these prompt chains has for us right the human in the loop is such a popular prompt chaining framework and frankly it's beyond overused right there are so many more creative ways to build out user experiences using different UI different uxs but also any one of these other different prompt chains or any combination of them that's the human in the loop you've seen that one you use it every single day when you interact with any one of these chat interfaces let's look at the self-correction agent real quick I'm just going to talk about the code I'll run it quickly so the self-correction prompt chain looks like this this is an idea we've explored on the channel before but essentially you have your prompt you execute based on the prompt if it's correct you're done your agent has completed its job if it's not correct you run an additional self-correction prompt and then you end and of course your self-correction can take many forms it can run in a loop it can run over and over but the idea is as simple as this execute if not successful self-correct right and this is really good for coding for executing for reviewing it's really great for improving on what's already been done okay great so this finished running in this simple example here we're looking for the right bash command that lets me list all files in the current directory super simple don't focus on that focus on the Chain the initial response is LS they we saer running the command I have this execution code in this case we're just doing another coin flip and then we're saying you know mock error so we're just kind of pretending like there's an error the core idea here is if your execution on your original prompt causes an error you then run a different code flow that self-corrects the previous run right so you can imagine if you're doing something like generating SQL or you're generating code or you're generating you know something that is executed against a functioning system AKA any function you can use this pattern to sell self-correct mistakes we did an entire video on this I'm going to link all the videos where we've covered some of these topics in more depth in the description as well as all this code I'm going to throw this in a gist so it's really simple to look at really simple to consume but that concludes seven prompt chains prompt workflows prompt flows prompt graphs prompt orchestrations whatever you want to call it that concludes seven prompt chains that you can use to build great AI agents powerful htic systems and you know new and interesting ideas we're really really beating this chat interface over the head it's definitely going to be here for a long time it's going to be here to stay but I think that there are more interesting innovative ways that we can you know build up products and also just build out really really great powerful agents underneath the hood right we said it a long time ago one prompt is not enough I think the llm industry and the software industry is really getting into that place where we're finally starting to dig into you know prompt orchestration and unlocking the power of different combinations of llms with our code with our data right we've talked about a lot of these topics before in the past I felt it was really important to bring these prompt chains back up and really highlight their capabilities to help you build great agentic software as I've been digging back into working on probably one of the most important agentic applications I'm going to build and that is my personal assistant let me know if you want me to share videos on how I'm thinking about designing and building my personal AI assistant there's a lot of really interesting ideas there and a lot of really interesting Concepts that we've built on the channel and some brand new Concepts that I'm still working through myself many of these ideas include you know building great prompt workflows using several of these prompt chains throughout filming this video we just finally hit the 10K Mark that's it guys we got 10K Subs I just want to shout out again everyone that's been following everyone that's been getting value out of the channel thank you so much for watching I really appreciate you being here let's continue to transform let's continue to evolve let's continue to use the best tools for the job using great engineering p patterns let's keep thinking Planning and Building together let's become a gench Engineers thanks so much for watching I'll see you in the next one
|
dec59f66354ffa88e2e41b3eab365223
|
{
"intermediate": 0.46654388308525085,
"beginner": 0.3292047381401062,
"expert": 0.20425140857696533
}
|
45,351
|
Разработать приложение, позволяющее строить график функции по
выбор пользователя. На выбор представить два графика 𝑦 = √𝑥 и 𝑦 =
𝑎
𝑥
.
Коэффициент a пользователь указывает через элемент класса NumericUpDown
только при выборе графика гиперболы на C# win form без combot box
|
919f76858a840ea133d1ae1bc02fec98
|
{
"intermediate": 0.3734325170516968,
"beginner": 0.40320873260498047,
"expert": 0.22335879504680634
}
|
45,352
|
I want to use awk to print the third field of the third line of a file if such file contains at least three lines (otherwise it would print something else). I need to do it in a one-liner and only using awk, not any other shell program. How can I do it?
|
28001fd5d75ed636a78dd42fe796f6ac
|
{
"intermediate": 0.5254055857658386,
"beginner": 0.1620483547449112,
"expert": 0.312546044588089
}
|
45,353
|
I want to use awk to print the third field of the third line of a file if such file contains at least three lines (otherwise it would print something else). I need to do it in a one-liner and only using awk, not any other shell program. How can I do it?
|
1dc009ba4a0077f2ec315caefaafc2d0
|
{
"intermediate": 0.5254055857658386,
"beginner": 0.1620483547449112,
"expert": 0.312546044588089
}
|
45,354
|
I want to use awk to print the third field of the third line of a file if such file contains at least three lines (otherwise it would print something else). I need to do it in a one-liner and only using awk, not any other shell program. How can I do it?
|
ad8fd24c422fddb362af1fd4a37c4c21
|
{
"intermediate": 0.5254055857658386,
"beginner": 0.1620483547449112,
"expert": 0.312546044588089
}
|
45,355
|
I want to use awk to print the third field of the third line of a file if such file contains at least three lines (otherwise it would print something else). I need to do it in a one-liner and only using awk, not any other shell program. How can I do it?
|
d15749dd3c4405ed7fc9b014335a557c
|
{
"intermediate": 0.5254055857658386,
"beginner": 0.1620483547449112,
"expert": 0.312546044588089
}
|
45,356
|
I have to visit parse tree and build symbol tables for functions by Antlr4.
I have to add some codes at SymbolTableVisitor class given behind with a skeleton code in B2CMain.cpp.
// B2CMain.cpp
#include <iostream>
#include <map>
#include <stack>
#include "antlr4-runtime.h"
#include "antlr4-cpp/BBaseVisitor.h"
#include "antlr4-cpp/BLexer.h"
#include "antlr4-cpp/BParser.h"
using namespace std;
using namespace antlr4;
using namespace antlr4::tree;
enum Types {tyAUTO, tyINT, tyDOUBLE, tySTRING, tyBOOL, tyCHAR, tyFUNCTION};
string mnemonicTypes[] = {"auto", "int", "double", "string", "bool", "char", "function"};
struct SymbolAttributes {
Types type; // int, double, bool, char, string, function --- auto if unknown yet
// if type == "function"
vector<Types> retArgTypes; // first element is a return_type
};
class SymbolTable {
private:
map<string, SymbolAttributes> table; // symbol-name: string, symbol-typeInfo: SymbolAttributes
public:
// Add a new symbol
void addSymbol(const string& name, const SymbolAttributes& attributes) {
table[name] = attributes;
}
// Check if a symbol exists
bool symbolExists(const string& name) const {
return table.find(name) != table.end();
}
// Get attributes of a symbol
SymbolAttributes getSymbolAttributes(const string& name) const {
if (symbolExists(name)) {
return table.at(name);
} else {
cout << "Error: Symbol " << name << " not found" << endl;
}
}
// Remove a symbol from the table
void removeSymbol(const string& name) {
table.erase(name);
}
// Print all symbols in the table (for debugging purposes)
void printSymbols() const {
for (const auto& pair : table) {
cout << "(name) " << pair.first << ", (type) " << mnemonicTypes[pair.second.type];
if (pair.second.type == tyFUNCTION) {
cout << " | ";
int n = pair.second.retArgTypes.size();
if (n > 0) {
cout << mnemonicTypes[pair.second.retArgTypes[0]] << "("; // return type
}
for (int i = 1; i < n-1; i++)
cout << mnemonicTypes[pair.second.retArgTypes[i]] << ", ";
if (n > 1) {
cout << mnemonicTypes[pair.second.retArgTypes[n-1]]; // last arg type
}
cout << ")";
}
cout << endl;
}
}
};
/*
* STEP 1. build symbol table
*/
const string _GlobalFuncName_ = "$_global_$";
// collection of per-function symbol tables accessed by function name
// symbol table in global scope can be accessed with special name defined in _GlobalFuncName_
map<string, SymbolTable*> symTabs;
class SymbolTableVisitor : public BBaseVisitor {
private:
int scopeLevel;
string curFuncName;
int blockCounter; // 추가 코드
public:
SymbolTableVisitor(): scopeLevel(0), blockCounter(1) {}; // 추가 코드
// Building symbol tables by visiting tree
any visitProgram(BParser::ProgramContext *ctx) override {
scopeLevel = 0; // global scope
// prepare symbol table for global scope
SymbolTable* globalSymTab = new SymbolTable();
curFuncName = _GlobalFuncName_;
symTabs[curFuncName] = globalSymTab;
// visit children
for (int i=0; i< ctx->children.size(); i++) {
visit(ctx->children[i]);
}
// print all symbol tables
for (auto& pair : symTabs) {
cout << "--- Symbol Table --- " << pair.first << endl; // function name
pair.second->printSymbols(); // per-function symbol table
cout << "";
}
return nullptr;
}
any visitDefinition(BParser::DefinitionContext *ctx) override {
visit(ctx->children[0]);
return nullptr;
}
any visitAutostmt(BParser::AutostmtContext *ctx) override {
// get current symbol table
SymbolTable *stab = symTabs[curFuncName];
// You can retrieve the variable names and constants using ctx->name(i) and ctx->constant(i)
for (int i=0, j=0; i < ctx->name().size(); i++) {
string varName = ctx->name(i)->getText();
enum Types varType = tyAUTO; // default type
// if initialized, get constant type
int idx_assn = 1 + i*2 + j*2 + 1; // auto name (= const)?, name (= const)?, ...
if (ctx->children[idx_assn]->getText().compare("=") == 0) {
if (ctx->constant(j)) {
varType = any_cast<Types>( visit(ctx->constant(j)) ); // returns init constant type
j++;
}
}
stab->addSymbol(varName, {varType});
}
return nullptr;
}
/* 추가 시작 ------------------------------------------------------------------------------------------ */
any visitBlockstmt(BParser::BlockstmtContext *ctx) override {
scopeLevel++;
string blockName = curFuncName + "_$" + to_string(blockCounter++);
SymbolTable* blockSymTab = new SymbolTable();
symTabs[blockName] = blockSymTab;
for (auto stmt : ctx->statement())
visit(stmt);
scopeLevel--;
return nullptr;
}
any visitIfstmt(BParser::IfstmtContext *ctx) override {
visit(ctx->expr());
visit(ctx->statement(0));
if (ctx->ELSE())
visit(ctx->statement(1));
return nullptr;
}
any visitWhilestmt(BParser::WhilestmtContext *ctx) override {
visit(ctx->expr());
visit(ctx->statement());
return nullptr;
}
any visitFuncdef(BParser::FuncdefContext *ctx) override {
string functionName = ctx->name(0)->getText();
curFuncName = functionName; // 현재 함수 이름
// Symbol Table 위한 type 저장하기
vector<Types> retArgTypes;
retArgTypes.push_back(tyAUTO);
for (int i = 1; i < ctx->name().size(); i++)
retArgTypes.push_back(tyAUTO);
// global의 Symbol Table
SymbolTable* globalSymTab = symTabs[_GlobalFuncName_];
globalSymTab->addSymbol(functionName, {tyFUNCTION, retArgTypes});
// 현재 함수의 Symbol Table
SymbolTable* funcSymTab = new SymbolTable();
symTabs[functionName] = funcSymTab;
visit(ctx->blockstmt()); // visit blockstmt
curFuncName = _GlobalFuncName_; // 함수 종료, Global로 돌아가기
return nullptr;
}
/* 추가 종료 ------------------------------------------------------------------------------------------ */
any visitConstant(BParser::ConstantContext *ctx) override {
if (ctx->INT()) return tyINT;
else if (ctx->REAL()) return tyDOUBLE;
else if (ctx->STRING()) return tySTRING;
else if (ctx->BOOL()) return tyBOOL;
else if (ctx->CHAR()) return tyCHAR;
cout << "[ERROR] unrecognizable constant is used for initialization: " << ctx->children[0]->getText() << endl;
exit(-1);
return nullptr;
}
};
/*
* STEP 2. infer type
*/
class TypeAnalysisVisitor : public BBaseVisitor {
public:
// infer types for 'auto' variables and functions
// ...
};
/*
* STEP 3. print code
*/
class PrintTreeVisitor : public BBaseVisitor {
public:
any visitProgram(BParser::ProgramContext *ctx) override {
// Perform some actions when visiting the program
for (int i=0; i< ctx->children.size(); i++) {
visit(ctx->children[i]);
}
return nullptr;
}
any visitDirective(BParser::DirectiveContext *ctx) override {
cout << ctx->SHARP_DIRECTIVE()->getText();
cout << endl;
return nullptr;
}
any visitDefinition(BParser::DefinitionContext *ctx) override {
visit(ctx->children[0]);
return nullptr;
}
any visitFuncdef(BParser::FuncdefContext *ctx) override {
// Handle function definition
string functionName = ctx->name(0)->getText();
cout << "auto " << functionName << "(" ;
// You can retrieve and visit the parameter list using ctx->name(i)
for (int i=1; i < ctx->name().size(); i++) {
if (i != 1) cout << ", ";
cout << "auto " << ctx->name(i)->getText();
}
cout << ")";
// visit blockstmt
visit(ctx->blockstmt());
return nullptr;
}
any visitStatement(BParser::StatementContext *ctx) override {
visit(ctx->children[0]);
return nullptr;
}
any visitAutostmt(BParser::AutostmtContext *ctx) override {
// You can retrieve the variable names and constants using ctx->name(i) and ctx->constant(i)
cout << "auto ";
for (int i=0, j=0; i < ctx->name().size(); i++) {
if (i != 0) cout << " ,";
cout << ctx->name(i)->getText();
int idx_assn = 1 + i*2 + j*2 + 1; // auto name (= const)?, name (= const)?, ...
if (ctx->children[idx_assn]->getText().compare("=") == 0) {
if (ctx->constant(j)) {
cout << " = ";
visit(ctx->constant(j));
j++;
}
}
}
cout << ";" << endl;
return nullptr;
}
any visitDeclstmt(BParser::DeclstmtContext *ctx) override {
// Handle function declaration
string functionName = ctx->name()->getText();
cout << "auto " << functionName << "(" ;
// You can retrieve and visit the parameter type list
for (int i=1; i < ctx->AUTO().size(); i++) {
if (i != 1) cout << ", ";
cout << "auto ";
}
cout << ");" << endl;
return nullptr;
}
any visitBlockstmt(BParser::BlockstmtContext *ctx) override {
// Perform some actions when visiting a block statement
cout << "{" << endl;
for (auto stmt : ctx->statement()) {
visit(stmt);
}
cout << "}" << endl;
return nullptr;
}
any visitIfstmt(BParser::IfstmtContext *ctx) override {
cout << "if (";
visit(ctx->expr());
cout << ") " ;
visit(ctx->statement(0));
if (ctx->ELSE()) {
cout << endl << "else ";
visit(ctx->statement(1));
}
return nullptr;
}
any visitWhilestmt(BParser::WhilestmtContext *ctx) override {
cout << "while (";
visit(ctx->expr());
cout << ") ";
visit(ctx->statement());
return nullptr;
}
any visitExpressionstmt(BParser::ExpressionstmtContext *ctx) override {
visit(ctx->expression());
cout << ";" << endl;
return nullptr;
}
any visitReturnstmt(BParser::ReturnstmtContext *ctx) override {
cout << "return";
if (ctx->expression()) {
cout << " (";
visit(ctx->expression());
cout << ")";
}
cout << ";" << endl;
return nullptr;
}
any visitNullstmt(BParser::NullstmtContext *ctx) override {
cout << ";" << endl;
return nullptr;
}
any visitExpr(BParser::ExprContext *ctx) override {
// unary operator
if(ctx->atom()) {
if (ctx->PLUS()) cout << "+";
else if (ctx->MINUS()) cout << "-";
else if (ctx->NOT()) cout << "!";
visit(ctx->atom());
}
// binary operator
else if (ctx->MUL() || ctx->DIV() || ctx->PLUS() || ctx->MINUS() ||
ctx->GT() || ctx->GTE() || ctx->LT() || ctx->LTE() || ctx->EQ() || ctx->NEQ() ||
ctx->AND() || ctx->OR() ) {
visit(ctx->expr(0));
cout << " " << ctx->children[1]->getText() << " "; // print binary operator
visit(ctx->expr(1));
}
// ternary operator
else if (ctx->QUEST()) {
visit(ctx->expr(0));
cout << " ? ";
visit(ctx->expr(1));
cout << " : ";
visit(ctx->expr(2));
}
else {
int lineNum = ctx->getStart()->getLine();
cerr << endl << "[ERROR] visitExpr: unrecognized ops in Line " << lineNum << " --" << ctx->children[1]->getText() << endl;
exit(-1); // error
}
return nullptr;
}
any visitAtom(BParser::AtomContext *ctx) override {
if (ctx->expression()) { // ( expression )
cout << "(";
visit(ctx->expression());
cout << ")";
}
else // name | constant | funcinvocation
visit(ctx->children[0]);
return nullptr;
}
any visitExpression(BParser::ExpressionContext *ctx) override {
if (ctx->ASSN()) { // assignment
visit(ctx->name());
cout << " = ";
}
visit(ctx->expr());
return nullptr;
}
any visitFuncinvocation(BParser::FuncinvocationContext *ctx) override {
cout << ctx->name()->getText() << "(";
for (int i=0; i < ctx->expr().size(); i++) {
if (i != 0) cout << ", ";
visit(ctx->expr(i));
}
cout << ")";
return nullptr;
}
any visitConstant(BParser::ConstantContext *ctx) override {
cout << ctx->children[0]->getText();
return nullptr;
}
any visitName(BParser::NameContext *ctx) override {
cout << ctx->NAME()->getText();
return nullptr;
}
};
int main(int argc, const char* argv[]) {
if (argc < 2) {
cerr << "[Usage] " << argv[0] << " <input-file>\n";
exit(0);
}
std::ifstream stream;
stream.open(argv[1]);
if (stream.fail()) {
cerr << argv[1] << " : file open fail\n";
exit(0);
}
//cout << "/*-- B2C ANTLR visitor --*/\n";
ANTLRInputStream inputStream(stream);
BLexer lexer(&inputStream);
CommonTokenStream tokenStream(&lexer);
BParser parser(&tokenStream);
ParseTree* tree = parser.program();
// STEP 1. visit parse tree and build symbol tables for functions (PA#1)
cout << endl << "/*** STEP 1. BUILD SYM_TABS *************" << endl;
SymbolTableVisitor SymtabTree;
SymtabTree.visit(tree);
cout << " --- end of step 1 ------------*/" << endl;
// STEP 2. visit parse tree and perform type inference for 'auto' typed variables and functions (PA#2)
cout << endl << "/*** STEP 2. ANALYZE TYPES *************" << endl;
TypeAnalysisVisitor AnalyzeTree;
AnalyzeTree.visit(tree);
cout << " --- end of step 2 ------------*/" << endl;
// STEP 3. visit parse tree and print out C code with correct types
cout << endl << "/*** STEP 3. TRANSFORM to C *************/" << endl;
PrintTreeVisitor PrintTree;
PrintTree.visit(tree);
return 0;
}
And this is the code of BParser.h
// Generated from B.g4 by ANTLR 4.13.1
#pragma once
#include "antlr4-runtime.h"
class BParser : public antlr4::Parser {
public:
enum {
T__0 = 1, T__1 = 2, T__2 = 3, T__3 = 4, T__4 = 5, AUTO = 6, PLUS = 7,
MINUS = 8, MUL = 9, DIV = 10, NOT = 11, GT = 12, GTE = 13, LT = 14,
LTE = 15, EQ = 16, NEQ = 17, AND = 18, OR = 19, QUEST = 20, COLON = 21,
SEMI = 22, IF = 23, ELSE = 24, WHILE = 25, RETURN = 26, ASSN = 27, BOOL = 28,
NAME = 29, INT = 30, REAL = 31, STRING = 32, CHAR = 33, SHARP_DIRECTIVE = 34,
BLOCKCOMMENT = 35, LINECOMMENT = 36, WS = 37
};
enum {
RuleProgram = 0, RuleDirective = 1, RuleDefinition = 2, RuleAutostmt = 3,
RuleDeclstmt = 4, RuleFuncdef = 5, RuleBlockstmt = 6, RuleStatement = 7,
RuleIfstmt = 8, RuleWhilestmt = 9, RuleExpressionstmt = 10, RuleReturnstmt = 11,
RuleNullstmt = 12, RuleExpr = 13, RuleAtom = 14, RuleExpression = 15,
RuleFuncinvocation = 16, RuleConstant = 17, RuleName = 18
};
explicit BParser(antlr4::TokenStream *input);
BParser(antlr4::TokenStream *input, const antlr4::atn::ParserATNSimulatorOptions &options);
~BParser() override;
std::string getGrammarFileName() const override;
const antlr4::atn::ATN& getATN() const override;
const std::vector<std::string>& getRuleNames() const override;
const antlr4::dfa::Vocabulary& getVocabulary() const override;
antlr4::atn::SerializedATNView getSerializedATN() const override;
class ProgramContext;
class DirectiveContext;
class DefinitionContext;
class AutostmtContext;
class DeclstmtContext;
class FuncdefContext;
class BlockstmtContext;
class StatementContext;
class IfstmtContext;
class WhilestmtContext;
class ExpressionstmtContext;
class ReturnstmtContext;
class NullstmtContext;
class ExprContext;
class AtomContext;
class ExpressionContext;
class FuncinvocationContext;
class ConstantContext;
class NameContext;
class ProgramContext : public antlr4::ParserRuleContext {
public:
ProgramContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *EOF();
std::vector<DirectiveContext *> directive();
DirectiveContext* directive(size_t i);
std::vector<DefinitionContext *> definition();
DefinitionContext* definition(size_t i);
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ProgramContext* program();
class DirectiveContext : public antlr4::ParserRuleContext {
public:
DirectiveContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *SHARP_DIRECTIVE();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
DirectiveContext* directive();
class DefinitionContext : public antlr4::ParserRuleContext {
public:
DefinitionContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
AutostmtContext *autostmt();
DeclstmtContext *declstmt();
FuncdefContext *funcdef();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
DefinitionContext* definition();
class AutostmtContext : public antlr4::ParserRuleContext {
public:
AutostmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *AUTO();
std::vector<NameContext *> name();
NameContext* name(size_t i);
antlr4::tree::TerminalNode *SEMI();
std::vector<antlr4::tree::TerminalNode *> ASSN();
antlr4::tree::TerminalNode* ASSN(size_t i);
std::vector<ConstantContext *> constant();
ConstantContext* constant(size_t i);
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
AutostmtContext* autostmt();
class DeclstmtContext : public antlr4::ParserRuleContext {
public:
DeclstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
std::vector<antlr4::tree::TerminalNode *> AUTO();
antlr4::tree::TerminalNode* AUTO(size_t i);
NameContext *name();
antlr4::tree::TerminalNode *SEMI();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
DeclstmtContext* declstmt();
class FuncdefContext : public antlr4::ParserRuleContext {
public:
FuncdefContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
std::vector<antlr4::tree::TerminalNode *> AUTO();
antlr4::tree::TerminalNode* AUTO(size_t i);
std::vector<NameContext *> name();
NameContext* name(size_t i);
BlockstmtContext *blockstmt();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
FuncdefContext* funcdef();
class BlockstmtContext : public antlr4::ParserRuleContext {
public:
BlockstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
std::vector<StatementContext *> statement();
StatementContext* statement(size_t i);
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
BlockstmtContext* blockstmt();
class StatementContext : public antlr4::ParserRuleContext {
public:
StatementContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
AutostmtContext *autostmt();
DeclstmtContext *declstmt();
BlockstmtContext *blockstmt();
IfstmtContext *ifstmt();
WhilestmtContext *whilestmt();
ExpressionstmtContext *expressionstmt();
ReturnstmtContext *returnstmt();
NullstmtContext *nullstmt();
DirectiveContext *directive();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
StatementContext* statement();
class IfstmtContext : public antlr4::ParserRuleContext {
public:
IfstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *IF();
ExprContext *expr();
std::vector<StatementContext *> statement();
StatementContext* statement(size_t i);
antlr4::tree::TerminalNode *ELSE();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
IfstmtContext* ifstmt();
class WhilestmtContext : public antlr4::ParserRuleContext {
public:
WhilestmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *WHILE();
ExprContext *expr();
StatementContext *statement();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
WhilestmtContext* whilestmt();
class ExpressionstmtContext : public antlr4::ParserRuleContext {
public:
ExpressionstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
ExpressionContext *expression();
antlr4::tree::TerminalNode *SEMI();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ExpressionstmtContext* expressionstmt();
class ReturnstmtContext : public antlr4::ParserRuleContext {
public:
ReturnstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *RETURN();
antlr4::tree::TerminalNode *SEMI();
ExpressionContext *expression();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ReturnstmtContext* returnstmt();
class NullstmtContext : public antlr4::ParserRuleContext {
public:
NullstmtContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *SEMI();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
NullstmtContext* nullstmt();
class ExprContext : public antlr4::ParserRuleContext {
public:
ExprContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
AtomContext *atom();
antlr4::tree::TerminalNode *PLUS();
antlr4::tree::TerminalNode *MINUS();
antlr4::tree::TerminalNode *NOT();
std::vector<ExprContext *> expr();
ExprContext* expr(size_t i);
antlr4::tree::TerminalNode *MUL();
antlr4::tree::TerminalNode *DIV();
antlr4::tree::TerminalNode *GT();
antlr4::tree::TerminalNode *GTE();
antlr4::tree::TerminalNode *LT();
antlr4::tree::TerminalNode *LTE();
antlr4::tree::TerminalNode *EQ();
antlr4::tree::TerminalNode *NEQ();
antlr4::tree::TerminalNode *AND();
antlr4::tree::TerminalNode *OR();
antlr4::tree::TerminalNode *QUEST();
antlr4::tree::TerminalNode *COLON();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ExprContext* expr();
ExprContext* expr(int precedence);
class AtomContext : public antlr4::ParserRuleContext {
public:
AtomContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
NameContext *name();
ConstantContext *constant();
ExpressionContext *expression();
FuncinvocationContext *funcinvocation();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
AtomContext* atom();
class ExpressionContext : public antlr4::ParserRuleContext {
public:
ExpressionContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
ExprContext *expr();
NameContext *name();
antlr4::tree::TerminalNode *ASSN();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ExpressionContext* expression();
class FuncinvocationContext : public antlr4::ParserRuleContext {
public:
FuncinvocationContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
NameContext *name();
std::vector<ExprContext *> expr();
ExprContext* expr(size_t i);
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
FuncinvocationContext* funcinvocation();
class ConstantContext : public antlr4::ParserRuleContext {
public:
ConstantContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *INT();
antlr4::tree::TerminalNode *REAL();
antlr4::tree::TerminalNode *STRING();
antlr4::tree::TerminalNode *BOOL();
antlr4::tree::TerminalNode *CHAR();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
ConstantContext* constant();
class NameContext : public antlr4::ParserRuleContext {
public:
NameContext(antlr4::ParserRuleContext *parent, size_t invokingState);
virtual size_t getRuleIndex() const override;
antlr4::tree::TerminalNode *NAME();
virtual std::any accept(antlr4::tree::ParseTreeVisitor *visitor) override;
};
NameContext* name();
bool sempred(antlr4::RuleContext *_localctx, size_t ruleIndex, size_t predicateIndex) override;
bool exprSempred(ExprContext *_localctx, size_t predicateIndex);
// By default the static state used to implement the parser is lazily initialized during the first
// call to the constructor. You can call this function if you wish to initialize the static state
// ahead of time.
static void initialize();
private:
};
This is a content of input0.b .
/* input0.b */
auto a, b = 10;
auto fn(auto, auto);
auto main()
{
auto i, j, phi = 3.14;
auto a;
{
auto x;
}
while(true) {
auto y;
if (y) {
auto z;
} else {
}
}
}
auto fn(auto x, auto y) {
}
When I execute this code with ./b2c input0.b ,
--- Symbol Table --- $_global_$
(name) a, (type) auto
(name) b, (type) int
(name) fn, (type) function | auto(auto, auto)
(name) main, (type) function | auto()
--- Symbol Table --- fn
--- Symbol Table --- fn_$6
--- Symbol Table --- main
(name) a, (type) auto
(name) i, (type) auto
(name) j, (type) auto
(name) phi, (type) double
(name) x, (type) auto
(name) y, (type) auto
(name) z, (type) auto
--- Symbol Table --- main_$1
--- Symbol Table --- main_$2
--- Symbol Table --- main_$3
--- Symbol Table --- main_$4
--- Symbol Table --- main_$5
is printed in Step 1.
I should modify the code to print the output
--- Symbol Table --- $_global_$
(name) a, (type) auto
(name) b, (type) int
(name) fn, (type) function | auto (auto, auto)
(name) main, (type) function | auto ()
--- Symbol Table --- main
(name) i, (type) auto
(name) j, (type) auto
(name) phi, (type) double
(name) a, (type) auto
--- Symbol Table --- main_$1
(name) x, (type) auto
--- Symbol Table --- main_$2
(name) y, (type) auto
--- Symbol Table --- main_$2_1
(name) z, (type) auto
--- Symbol Table --- main_$2_2
--- Symbol Table --- fn
(name) x, (type) auto
(name) y, (type) auto
to be printed in Step 1.
I think I have to control all the scopes (such as if statement, while statement, {}, ...) in making symbol table. How can I modify this SymbolTableVisitor class code to activate the output in Step 1 correctly?
|
7d29e8d4be793e0e0afd62f0d222b5d7
|
{
"intermediate": 0.3797183036804199,
"beginner": 0.4526875913143158,
"expert": 0.16759416460990906
}
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45,357
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I need an awk one-liner to print me the third field of the second line only if the file contains three or more lines (otherwise it would print someting else)
|
f2e5001b4e7e6145ef1faee2fe19c3ee
|
{
"intermediate": 0.4675501585006714,
"beginner": 0.17232142388820648,
"expert": 0.36012840270996094
}
|
45,358
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Please adjust the following code to include common 16:9 and 4:3 ratio resolutions, such as 4K: -filter_complex "[0:v]split=2[v0][v1];[v0]scale_vaapi=w=1920:h=1080[v0out];[v1]scale_vaapi=w=1280:h=720[v1out]" -map [v0out]
|
0bb456be01cae94abe0dea3a76b2a3ef
|
{
"intermediate": 0.402480810880661,
"beginner": 0.28889867663383484,
"expert": 0.30862054228782654
}
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45,359
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code a snake game in python
|
2a64f4ef5413ffc5cad48cdd91b60837
|
{
"intermediate": 0.3339133560657501,
"beginner": 0.3009132146835327,
"expert": 0.36517345905303955
}
|
45,360
|
code a snake game in python
|
01cec2c90c5b316e88879432711e72ab
|
{
"intermediate": 0.3339133560657501,
"beginner": 0.3009132146835327,
"expert": 0.36517345905303955
}
|
45,361
|
Can you recode this website with a darker theme https://wanderinggnomeminerals.com
|
6ba02cd87aaf973b791628a5bfe5b701
|
{
"intermediate": 0.33203452825546265,
"beginner": 0.28579434752464294,
"expert": 0.3821711242198944
}
|
45,362
|
In a function definition, the parameters without default values must _________ the parameters with default values.
In a function definition, the parameters without default values must _________ the parameters with default values.
not outnumber
precede
follow
outnumber
|
d5330a73cf57325ecf1904edf7ae258c
|
{
"intermediate": 0.22533410787582397,
"beginner": 0.48827052116394043,
"expert": 0.2863953411579132
}
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45,363
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How to install plugins in django cms deployed on vercel
|
1cf22be32ec7b91b71da81b9bd35bd74
|
{
"intermediate": 0.7913557887077332,
"beginner": 0.09890282154083252,
"expert": 0.10974139720201492
}
|
45,364
|
I need help with an excel formula. I have dates in cell A3 and different products' prices on the other columns. In cells from B2 to D2 i have the product names, while in cells from B3 to D3 their corresponding prices. I need a formula to find a specific product price based on the most recent data.
|
40650336b88fdff09d5e78b3b272cf39
|
{
"intermediate": 0.41246363520622253,
"beginner": 0.27756717801094055,
"expert": 0.3099692165851593
}
|
45,365
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I need help with an excel formula. I have dates in column A and different products’ prices on the other columns with cells from B2 to D2 with their corresponding product's names so the actual prices starts from B3). I need a formula to find a specific product price based on its name and the most recent data.
|
170241254336978e78a88e35749eb811
|
{
"intermediate": 0.3776162266731262,
"beginner": 0.2823166847229004,
"expert": 0.34006714820861816
}
|
45,366
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make a variable choice field category and choice variable subcategory and make subcategory depend on the category variable choices in servicenow
|
aead7e598faec3a036f74793c42d1927
|
{
"intermediate": 0.3858908712863922,
"beginner": 0.18673129379749298,
"expert": 0.427377849817276
}
|
45,367
|
How to make 2 aggregate columns pivot mssql
|
2624d802bd11293a6728acda9fa5e3b0
|
{
"intermediate": 0.4280567169189453,
"beginner": 0.23150140047073364,
"expert": 0.34044182300567627
}
|
45,368
|
Here is some job data I receive from the backend. I'm writing a frontend in ClojureScript, react, and reframe to handle it:
[{:wip-journaled nil, :wip-expensed nil, :job-id “58b04b8b-e481-459d-88a6-96525edd3262”, :uses-wip false, :total-draft-invoice-amount 0, :completed false, :total-approved-invoice-amount 0, :customer “City Limousines”, :actual-cost {:profession-cost 8684, :inventory-cost 1496, :asset-cost 1875}, :reference “J00001”, :update-ts #inst “2024-03-25T01:23:40.178-00:00”, :quote {:approved? false, :job-id “58b04b8b-e481-459d-88a6-96525edd3262”, :org-id “fc3433bb-c881-493c-89b2-dc36e7a5f6cc”, :currency-code “AUD”, :reference “1 - J00001 - Quote 1”, :total 58300, :id “597a798d-8f16-49ba-87df-a1f9a1b605bc”, :create-ts #inst “2024-03-25T00:41:32.285-00:00”, :sent? false}, :create-ts #inst “2024-03-25T01:23:40.178-00:00”, :estimated-cost 27251, :latest-invoice-ts nil} {:wip-journaled nil, :wip-expensed nil, :job-id “3e6e6c76-7dfd-48eb-afdc-6f496906a929”, :uses-wip false, :total-draft-invoice-amount 0, :completed false, :total-approved-invoice-amount 33000, :customer “Bayside Club”, :actual-cost {:profession-cost 0, :inventory-cost 8000, :asset-cost 2500}, :reference “J00002”, :update-ts #inst “2024-04-05T03:08:56.065-00:00”, :quote {:approved? false, :job-id “3e6e6c76-7dfd-48eb-afdc-6f496906a929”, :org-id “fc3433bb-c881-493c-89b2-dc36e7a5f6cc”, :currency-code “AUD”, :reference “3 - J00002”, :total 59520, :id “335193f9-b433-4231-ba5e-833d0614c391”, :create-ts #inst “2024-04-05T00:55:44.673-00:00”, :sent? false}, :create-ts #inst “2024-04-03T23:05:44.656-00:00”, :estimated-cost 28380, :latest-invoice-ts #inst “2024-04-05T00:59:53.357-00:00”} {:wip-journaled nil, :wip-expensed nil, :job-id “a743e4c6-23a6-4c6f-b827-c972bd9df5c2”, :uses-wip false, :total-draft-invoice-amount 70548, :completed false, :total-approved-invoice-amount 0, :customer “Abby & Wells”, :actual-cost {:profession-cost 0, :inventory-cost 8000, :asset-cost 0}, :reference “J00006”, :update-ts #inst “2024-04-05T03:15:25.939-00:00”, :quote nil, :create-ts #inst “2024-04-05T03:15:07.994-00:00”, :estimated-cost 22800, :latest-invoice-ts #inst “2024-04-05T03:18:30.983-00:00”}]
I’m able to extract the month numbers for each job with (util/job-month-numbers job-list). For the above data, it gives me (3 4 4). I want to sum the :total-approved-invoice-amount for each job in a given month. I want it formatted like [20323 85112]. Can you finish the function for me?
|
41fdc18c3e40c715fa5c577de8790065
|
{
"intermediate": 0.3849543035030365,
"beginner": 0.46255382895469666,
"expert": 0.15249179303646088
}
|
45,369
|
i use cdn. i set sub domain to port 8000 of my django project. when run site tell me CSRF error
|
cdcd3639f6724db079fdaa22f2f20be6
|
{
"intermediate": 0.43983563780784607,
"beginner": 0.22476859390735626,
"expert": 0.3353957235813141
}
|
45,370
|
Create a bash file for after a archlinux install and install top Python packages for data science purposes and use pip
|
ccb476fe2911b54387dba9073daf0390
|
{
"intermediate": 0.3665735423564911,
"beginner": 0.10675986856222153,
"expert": 0.5266666412353516
}
|
45,371
|
Create a bash file for after a archlinux install and install the top Python packages for data science purposes. Include the installation of R and RStudio server and jupyter notebooks. Also docker.
|
f1fea406b46321de5e0140f7158adc51
|
{
"intermediate": 0.37038666009902954,
"beginner": 0.11055219918489456,
"expert": 0.5190612077713013
}
|
45,372
|
how to write unit test using google for the following function int snip(const struct point* pnt,int u,int v,int w,int n,int *V) {
int p;
float Ax, Ay, Bx, By, Cx, Cy, Px, Py;
Ax = pnt[V[u]].x;
Ay = pnt[V[u]].y;
Bx = pnt[V[v]].x;
By = pnt[V[v]].y;
Cx = pnt[V[w]].x;
Cy = pnt[V[w]].y;
if ( (((Bx-Ax)*(Cy-Ay)) - ((By-Ay)*(Cx-Ax))) < 0.f )
return 0;
for (p=0; p<n; p++) {
if( (p == u) || (p == v) || (p == w) )
continue;
Px = pnt[V[p]].x;
Py = pnt[V[p]].y;
if (inside_triangle(Ax,Ay,Bx,By,Cx,Cy,Px,Py))
return 0;
}
return 1;
}
|
38dee65caf6dba699504a50dee11d194
|
{
"intermediate": 0.2543475031852722,
"beginner": 0.47015392780303955,
"expert": 0.27549856901168823
}
|
45,373
|
how can i cat all the files in a directory
|
3d0152bd1520b40612458661a7a38be4
|
{
"intermediate": 0.33038637042045593,
"beginner": 0.2897877097129822,
"expert": 0.37982597947120667
}
|
45,374
|
can you correct this code:from fastapi import FastAPI
from fastapi.responses import RedirectResponse
from langserve import add_routes
from retrieval_agent_fireworks import agent_executor as retrieval_agent_fireworks_chain
app = FastAPI()
@app.get("/")
async def redirect_root_to_docs():
return RedirectResponse("/docs")
# Edit this to add the chain you want to add
add_routes(app, retrieval_agent_fireworks_chain, path="\retrieval-agent-fireworks")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
|
cb38a357eaa3ef39e169cfb253f3aecb
|
{
"intermediate": 0.6583682298660278,
"beginner": 0.18851442635059357,
"expert": 0.1531173586845398
}
|
45,375
|
What are options field of ipv4 protocol? What can be minimum and maximum size if the field?
|
2302f0947a91dd385427fb4fc7ba69ae
|
{
"intermediate": 0.3271831274032593,
"beginner": 0.26921531558036804,
"expert": 0.4036015272140503
}
|
45,376
|
how do i use the following bootstrap pie chart to display the totals of each package in nursing school and nursing prep: <div class="card-body">
<h5 class="card-title">Pie Chart</h5>
<canvas id="chart-area"></canvas>
</div> CountData.php: <?php
// Count All Course
$selCourse = $conn->query("SELECT COUNT(cou_id) as totCourse FROM course_tbl ")->fetch(PDO::FETCH_ASSOC);
// Count All Exam
$selExam = $conn->query("SELECT COUNT(ex_id) as totExam FROM exam_tbl WHERE examStatus='active'")->fetch(PDO::FETCH_ASSOC);
$selSchool = $conn->query("SELECT COUNT(sqt_id) as totSco FROM school_question_tbl WHERE school_status='active'")->fetch(PDO::FETCH_ASSOC);
// Count All Examinee
$selExaminee = $conn->query("SELECT COUNT(exmne_id) as totExaminee FROM examinee_tbl ")->fetch(PDO::FETCH_ASSOC);
// Count All Packages
$selPrepCM = $conn->query("SELECT COUNT(exmne_id) as totPrepCM FROM examinee_tbl WHERE exmne_package = 'PREP COURSES MONTHLY'")->fetch(PDO::FETCH_ASSOC);
// Count All PREP COURSES 6 MONTHS
$selPrepC6M = $conn->query("SELECT COUNT(exmne_id) as totPrepC6M FROM examinee_tbl WHERE exmne_package = 'PREP COURSES 6 MONTHS'")->fetch(PDO::FETCH_ASSOC);
// Count All PREP COURSES 1 YEAR
$selPrepC1Y = $conn->query("SELECT COUNT(exmne_id) as totPrepC1Y FROM examinee_tbl WHERE exmne_package = 'PREP COURSES 1 YEAR'")->fetch(PDO::FETCH_ASSOC);
// Count All NURSING SCHOOL MONTHLY
$selNursingSM = $conn->query("SELECT COUNT(exmne_id) as totNursingSM FROM examinee_tbl WHERE exmne_package = 'NURSING SCHOOL MONTHLY'")->fetch(PDO::FETCH_ASSOC);
// Count All NURSING SCHOOL 6 MONTHS
$selNursingS6M = $conn->query("SELECT COUNT(exmne_id) as totNursingS6M FROM examinee_tbl WHERE exmne_package = 'NURSING SCHOOL 6 MONTHS'")->fetch(PDO::FETCH_ASSOC);
// Count All NURSING SCHOOL 1 YEAR
$selNursingS1Y = $conn->query("SELECT COUNT(exmne_id) as totNursingS1Y FROM examinee_tbl WHERE exmne_package = 'NURSING SCHOOL 1 YEAR'")->fetch(PDO::FETCH_ASSOC);
// Count All NURSING SCHOOL 2 YEARS
$selNursingS2Y = $conn->query("SELECT COUNT(exmne_id) as totNursingS2Y FROM examinee_tbl WHERE exmne_package = 'NURSING SCHOOL 2 YEARS'")->fetch(PDO::FETCH_ASSOC);
// Count total number of expired subscriptions
$selExpired = $conn->query("SELECT COUNT(exmne_id) as totExpired FROM examinee_tbl WHERE exmne_status = 'EXPIRED'")->fetch(PDO::FETCH_ASSOC);
$expiredPercentage = 0;
if ($selExam['totExam'] != 0) {
$expiredPercentage = round(($selExpired['totExpired'] / $selExaminee['totExaminee']) * 100);
}
// Count total number of paid subscriptions
$selPaid = $conn->query("SELECT COUNT(exmne_id) as totPaid FROM examinee_tbl WHERE exmne_status = 'PAID'")->fetch(PDO::FETCH_ASSOC);
$paidPercentage = 0;
if ($selExam['totExam'] != 0) {
$paidPercentage = round(($selPaid['totPaid'] / $selExaminee['totExaminee']) * 100);
}
?>
|
8e273b160a3df3d6fb78cc82e6a584d3
|
{
"intermediate": 0.31378015875816345,
"beginner": 0.43218016624450684,
"expert": 0.2540396749973297
}
|
45,377
|
convert Vnet to Unet
import torch
import time
from torch import nn
import numpy as np
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i == 0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out, track_running_stats=False))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class ResidualConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ResidualConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i == 0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out, track_running_stats=False))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
if i != n_stages - 1:
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = (self.conv(x) + x)
x = self.relu(x)
return x
class DownsamplingConvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(DownsamplingConvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out, track_running_stats=False))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class UpsamplingDeconvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(UpsamplingDeconvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out, track_running_stats=False))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class Upsampling(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(Upsampling, self).__init__()
ops = []
ops.append(nn.Upsample(scale_factor=stride, mode='trilinear', align_corners=False))
ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out, track_running_stats=False))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class VNet_singleTooth(nn.Module):
def __init__(self, n_channels=2, n_classes=2, n_filters=16, normalization='none', has_dropout=False):
super(VNet_singleTooth, self).__init__()
self.has_dropout = has_dropout
self.block_one = ConvBlock(1, n_channels, n_filters, normalization=normalization)
self.block_one_dw = DownsamplingConvBlock(n_filters, n_filters, normalization=normalization)
self.block_two = ConvBlock(2, n_filters, n_filters * 2, normalization=normalization)
self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization)
self.block_three = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization)
self.block_four = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization)
self.block_five = ConvBlock(3, n_filters * 16, n_filters * 16, normalization=normalization)
self.block_five_up = UpsamplingDeconvBlock(n_filters * 16, n_filters * 8, normalization=normalization)
self.block_six = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_six_up = UpsamplingDeconvBlock(n_filters * 8, n_filters * 4, normalization=normalization)
self.block_seven = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_seven_up = UpsamplingDeconvBlock(n_filters * 4, n_filters * 2, normalization=normalization)
self.block_eight = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_eight_up = UpsamplingDeconvBlock(n_filters * 2, n_filters, normalization=normalization)
self.out_conv_seg = nn.Conv3d(n_filters, 2, 3, padding=1)
def encoder(self, input):
x1 = self.block_one(input)
x1_dw = self.block_one_dw(x1)
x2 = self.block_two(x1_dw)
x2_dw = self.block_two_dw(x2)
x3 = self.block_three(x2_dw)
x3_dw = self.block_three_dw(x3)
x4 = self.block_four(x3_dw)
x4_dw = self.block_four_dw(x4)
x5 = self.block_five(x4_dw)
# x5 = F.dropout3d(x5, p=0.5, training=True)
# if self.has_dropout:
# x5 = self.dropout(x5)
res = [x2, x3, x4, x5]
return res
def decoder(self, features):
x2 = features[0]
x3 = features[1]
x4 = features[2]
x5 = features[3]
# x5 = features[4]
x5_up = self.block_five_up(x5)
x5_up = x5_up + x4
x6 = self.block_six(x5_up)
x6_up = self.block_six_up(x6)
x6_up = x6_up + x3
x7 = self.block_seven(x6_up)
x7_up = self.block_seven_up(x7)
x7_up = x7_up + x2
x8 = self.block_eight(x7_up)
x8_up = self.block_eight_up(x8)
out_seg = self.out_conv_seg(x8_up)
return out_seg
def forward(self, ori):
ori = torch.reshape(ori, (ori.shape[0] * ori.shape[1], 1, ori.shape[2], ori.shape[3], ori.shape[4]))
features = self.encoder(ori)
seg = self.decoder(features)
return seg
|
9a0e9be0c79543e06bf878c9ee38ad7d
|
{
"intermediate": 0.25645673274993896,
"beginner": 0.5961920022964478,
"expert": 0.1473512202501297
}
|
45,378
|
Convert this Vnet to UNET
import torch
from torch import nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i==0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class ResidualConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ResidualConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i == 0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
if i != n_stages-1:
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = (self.conv(x) + x)
x = self.relu(x)
return x
class DownsamplingConvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(DownsamplingConvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class UpsamplingDeconvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(UpsamplingDeconvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class Upsampling(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(Upsampling, self).__init__()
ops = []
ops.append(nn.Upsample(scale_factor=stride, mode='trilinear',align_corners=False))
ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class VNet(nn.Module):
def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False):
super(VNet, self).__init__()
self.has_dropout = has_dropout
self.block_one = ConvBlock(1, n_channels, n_filters, normalization=normalization)
self.block_one_dw = DownsamplingConvBlock(n_filters, 2 * n_filters, normalization=normalization)
self.block_two = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization)
self.block_three = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization)
self.block_four = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization)
self.block_five = ConvBlock(3, n_filters * 16, n_filters * 16, normalization=normalization)
self.block_five_up = UpsamplingDeconvBlock(n_filters * 16, n_filters * 8, normalization=normalization)
self.block_six = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_six_up = UpsamplingDeconvBlock(n_filters * 8, n_filters * 4, normalization=normalization)
self.block_seven = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_seven_up = UpsamplingDeconvBlock(n_filters * 4, n_filters * 2, normalization=normalization)
self.block_eight = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_eight_up = UpsamplingDeconvBlock(n_filters * 2, n_filters, normalization=normalization)
self.block_nine = ConvBlock(1, n_filters, n_filters, normalization=normalization)
self.out_conv = nn.Conv3d(n_filters, n_classes, 1, padding=0)
self.dropout = nn.Dropout3d(p=0.5, inplace=False)
# self.__init_weight()
def encoder(self, input):
x1 = self.block_one(input)
x1_dw = self.block_one_dw(x1)
x2 = self.block_two(x1_dw)
x2_dw = self.block_two_dw(x2)
x3 = self.block_three(x2_dw)
x3_dw = self.block_three_dw(x3)
x4 = self.block_four(x3_dw)
x4_dw = self.block_four_dw(x4)
x5 = self.block_five(x4_dw)
# x5 = F.dropout3d(x5, p=0.5, training=True)
if self.has_dropout:
x5 = self.dropout(x5)
res = [x1, x2, x3, x4, x5]
return res
def decoder(self, features):
x1 = features[0]
x2 = features[1]
x3 = features[2]
x4 = features[3]
x5 = features[4]
x5_up = self.block_five_up(x5)
x5_up = x5_up + x4
x6 = self.block_six(x5_up)
x6_up = self.block_six_up(x6)
x6_up = x6_up + x3
x7 = self.block_seven(x6_up)
x7_up = self.block_seven_up(x7)
x7_up = x7_up + x2
x8 = self.block_eight(x7_up)
x8_up = self.block_eight_up(x8)
x8_up = x8_up + x1
x9 = self.block_nine(x8_up)
# x9 = F.dropout3d(x9, p=0.5, training=True)
if self.has_dropout:
x9 = self.dropout(x9)
out = self.out_conv(x9)
return out
def forward(self, input, turnoff_drop=False):
if turnoff_drop:
has_dropout = self.has_dropout
self.has_dropout = False
features = self.encoder(input)
out = self.decoder(features)
if turnoff_drop:
self.has_dropout = has_dropout
return out
|
ad016b2de83eb2e7c4f811230160fcc5
|
{
"intermediate": 0.26622527837753296,
"beginner": 0.5821629166603088,
"expert": 0.15161177515983582
}
|
45,379
|
convert vnet to unet
import torch
from torch import nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i==0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class ResidualConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ResidualConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i == 0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
if i != n_stages-1:
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = (self.conv(x) + x)
x = self.relu(x)
return x
class DownsamplingConvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(DownsamplingConvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class UpsamplingDeconvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(UpsamplingDeconvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class Upsampling(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(Upsampling, self).__init__()
ops = []
ops.append(nn.Upsample(scale_factor=stride, mode='trilinear',align_corners=False))
ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class VNet(nn.Module):
def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False):
super(VNet, self).__init__()
self.has_dropout = has_dropout
self.block_one = ConvBlock(1, n_channels, n_filters, normalization=normalization)
self.block_one_dw = DownsamplingConvBlock(n_filters, 2 * n_filters, normalization=normalization)
self.block_two = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization)
self.block_three = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization)
self.block_four = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization)
self.block_five = ConvBlock(3, n_filters * 16, n_filters * 16, normalization=normalization)
self.block_five_up = UpsamplingDeconvBlock(n_filters * 16, n_filters * 8, normalization=normalization)
self.block_six = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_six_up = UpsamplingDeconvBlock(n_filters * 8, n_filters * 4, normalization=normalization)
self.block_seven = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_seven_up = UpsamplingDeconvBlock(n_filters * 4, n_filters * 2, normalization=normalization)
self.block_eight = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_eight_up = UpsamplingDeconvBlock(n_filters * 2, n_filters, normalization=normalization)
self.block_nine = ConvBlock(1, n_filters, n_filters, normalization=normalization)
self.out_conv = nn.Conv3d(n_filters, n_classes, 1, padding=0)
self.dropout = nn.Dropout3d(p=0.5, inplace=False)
# self.__init_weight()
def encoder(self, input):
x1 = self.block_one(input)
x1_dw = self.block_one_dw(x1)
x2 = self.block_two(x1_dw)
x2_dw = self.block_two_dw(x2)
x3 = self.block_three(x2_dw)
x3_dw = self.block_three_dw(x3)
x4 = self.block_four(x3_dw)
x4_dw = self.block_four_dw(x4)
x5 = self.block_five(x4_dw)
# x5 = F.dropout3d(x5, p=0.5, training=True)
if self.has_dropout:
x5 = self.dropout(x5)
res = [x1, x2, x3, x4, x5]
return res
def decoder(self, features):
x1 = features[0]
x2 = features[1]
x3 = features[2]
x4 = features[3]
x5 = features[4]
x5_up = self.block_five_up(x5)
x5_up = x5_up + x4
x6 = self.block_six(x5_up)
x6_up = self.block_six_up(x6)
x6_up = x6_up + x3
x7 = self.block_seven(x6_up)
x7_up = self.block_seven_up(x7)
x7_up = x7_up + x2
x8 = self.block_eight(x7_up)
x8_up = self.block_eight_up(x8)
x8_up = x8_up + x1
x9 = self.block_nine(x8_up)
# x9 = F.dropout3d(x9, p=0.5, training=True)
if self.has_dropout:
x9 = self.dropout(x9)
out = self.out_conv(x9)
return out
def forward(self, input, turnoff_drop=False):
if turnoff_drop:
has_dropout = self.has_dropout
self.has_dropout = False
features = self.encoder(input)
out = self.decoder(features)
if turnoff_drop:
self.has_dropout = has_dropout
return out
|
53e534b60e49ef5cbfe06a6565cc839c
|
{
"intermediate": 0.2189186066389084,
"beginner": 0.6035993099212646,
"expert": 0.17748212814331055
}
|
45,380
|
Convert VNet to 3D UNet Medical image
import torch
from torch import nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i==0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class ResidualConvBlock(nn.Module):
def __init__(self, n_stages, n_filters_in, n_filters_out, normalization='none'):
super(ResidualConvBlock, self).__init__()
ops = []
for i in range(n_stages):
if i == 0:
input_channel = n_filters_in
else:
input_channel = n_filters_out
ops.append(nn.Conv3d(input_channel, n_filters_out, 3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
if i != n_stages-1:
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = (self.conv(x) + x)
x = self.relu(x)
return x
class DownsamplingConvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(DownsamplingConvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.Conv3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class UpsamplingDeconvBlock(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(UpsamplingDeconvBlock, self).__init__()
ops = []
if normalization != 'none':
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
else:
assert False
else:
ops.append(nn.ConvTranspose3d(n_filters_in, n_filters_out, stride, padding=0, stride=stride))
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class Upsampling(nn.Module):
def __init__(self, n_filters_in, n_filters_out, stride=2, normalization='none'):
super(Upsampling, self).__init__()
ops = []
ops.append(nn.Upsample(scale_factor=stride, mode='trilinear',align_corners=False))
ops.append(nn.Conv3d(n_filters_in, n_filters_out, kernel_size=3, padding=1))
if normalization == 'batchnorm':
ops.append(nn.BatchNorm3d(n_filters_out))
elif normalization == 'groupnorm':
ops.append(nn.GroupNorm(num_groups=16, num_channels=n_filters_out))
elif normalization == 'instancenorm':
ops.append(nn.InstanceNorm3d(n_filters_out))
elif normalization != 'none':
assert False
ops.append(nn.ReLU(inplace=True))
self.conv = nn.Sequential(*ops)
def forward(self, x):
x = self.conv(x)
return x
class VNet(nn.Module):
def __init__(self, n_channels=3, n_classes=2, n_filters=16, normalization='none', has_dropout=False):
super(VNet, self).__init__()
self.has_dropout = has_dropout
self.block_one = ConvBlock(1, n_channels, n_filters, normalization=normalization)
self.block_one_dw = DownsamplingConvBlock(n_filters, 2 * n_filters, normalization=normalization)
self.block_two = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_two_dw = DownsamplingConvBlock(n_filters * 2, n_filters * 4, normalization=normalization)
self.block_three = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_three_dw = DownsamplingConvBlock(n_filters * 4, n_filters * 8, normalization=normalization)
self.block_four = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_four_dw = DownsamplingConvBlock(n_filters * 8, n_filters * 16, normalization=normalization)
self.block_five = ConvBlock(3, n_filters * 16, n_filters * 16, normalization=normalization)
self.block_five_up = UpsamplingDeconvBlock(n_filters * 16, n_filters * 8, normalization=normalization)
self.block_six = ConvBlock(3, n_filters * 8, n_filters * 8, normalization=normalization)
self.block_six_up = UpsamplingDeconvBlock(n_filters * 8, n_filters * 4, normalization=normalization)
self.block_seven = ConvBlock(3, n_filters * 4, n_filters * 4, normalization=normalization)
self.block_seven_up = UpsamplingDeconvBlock(n_filters * 4, n_filters * 2, normalization=normalization)
self.block_eight = ConvBlock(2, n_filters * 2, n_filters * 2, normalization=normalization)
self.block_eight_up = UpsamplingDeconvBlock(n_filters * 2, n_filters, normalization=normalization)
self.block_nine = ConvBlock(1, n_filters, n_filters, normalization=normalization)
self.out_conv = nn.Conv3d(n_filters, n_classes, 1, padding=0)
self.dropout = nn.Dropout3d(p=0.5, inplace=False)
# self.__init_weight()
def encoder(self, input):
x1 = self.block_one(input)
x1_dw = self.block_one_dw(x1)
x2 = self.block_two(x1_dw)
x2_dw = self.block_two_dw(x2)
x3 = self.block_three(x2_dw)
x3_dw = self.block_three_dw(x3)
x4 = self.block_four(x3_dw)
x4_dw = self.block_four_dw(x4)
x5 = self.block_five(x4_dw)
# x5 = F.dropout3d(x5, p=0.5, training=True)
if self.has_dropout:
x5 = self.dropout(x5)
res = [x1, x2, x3, x4, x5]
return res
def decoder(self, features):
x1 = features[0]
x2 = features[1]
x3 = features[2]
x4 = features[3]
x5 = features[4]
x5_up = self.block_five_up(x5)
x5_up = x5_up + x4
x6 = self.block_six(x5_up)
x6_up = self.block_six_up(x6)
x6_up = x6_up + x3
x7 = self.block_seven(x6_up)
x7_up = self.block_seven_up(x7)
x7_up = x7_up + x2
x8 = self.block_eight(x7_up)
x8_up = self.block_eight_up(x8)
x8_up = x8_up + x1
x9 = self.block_nine(x8_up)
# x9 = F.dropout3d(x9, p=0.5, training=True)
if self.has_dropout:
x9 = self.dropout(x9)
out = self.out_conv(x9)
return out
def forward(self, input, turnoff_drop=False):
if turnoff_drop:
has_dropout = self.has_dropout
self.has_dropout = False
features = self.encoder(input)
out = self.decoder(features)
if turnoff_drop:
self.has_dropout = has_dropout
return out
|
39d7379e8bb3756f7807e7b816e2d8a5
|
{
"intermediate": 0.27877187728881836,
"beginner": 0.5017614364624023,
"expert": 0.21946673095226288
}
|
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