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{
    "configurations": [
        {
            "type": "OctaveDebugger",
            "request": "launch",
            "name": "Octave Program",
            "program": "${workspaceFolder}/${fileBasenameNoExtension}.m",
            "octave": "octave-cli",
            "sourceFolder": "${workspaceFolder}",
            "autoTerminate": true,

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disp("multi-class classification with softmax regression from scratch......");

function [h, display_array] = displayData(X, example_width)
    %DISPLAYDATA Display 2D data in a nice grid
    %   [h, display_array] = DISPLAYDATA(X, example_width) displays 2D data
    %   stored in X in a nice grid. It returns the figure handle h and the
    %   displayed array if requested.

    % Set example_width automatically if not passed in
    if ~exist('example_width', 'var') || isempty(example_width)

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disp("The numerical gradient check......");

function W = randWeights(in, out)
    epsilon_init = sqrt(6) / sqrt(in + out);
    W = rand(in, out) * 2 * epsilon_init - epsilon_init;
endfunction

function numericalGrads = applyNumericalGradients(costFunc, nn_params)
    % Apply partial derivative of costFunc with respect to the
    %        i-th input argument.

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disp("k-nearest neighbors algorithm (Simple and plain implementation)");

function [prediction, selected_by_K, distances] = kNN(X, model, K, MODE)
    % X is a feature vector to query data in model with nearest distance.
    % model contains the "trained" data, actually for KNN we don't train and there aren't parameters,
    %       we compare distance with the ground-truth data.
    %       Last column of model is label column.
    % K is the first K result to select.
    % MODE for "regression" , "classification" or "sortOnly".

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disp("Use the normal equations without training to get linear regression weights...");

function W = randWeights(in, out)
    epsilon_init = sqrt(6) / sqrt(in + out);
    W = rand(in, out) * 2 * epsilon_init - epsilon_init;
endfunction

% Gen data with m rows and n feature columns.
m = 5000; % m rows data
n = 100; % input features numbers

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extension StreamControllerExtension<T> on StreamController<T> {
  setStreamListener(void onData(T event),
      {Function onError, void onDone(), bool cancelOnError}) {
    if (!this.hasListener) {
      this.stream.listen(onData,
          onError: onError, onDone: onDone, cancelOnError: cancelOnError);
    }
  }
}

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Wrap_content ,Wrap_content :

 //use this as child
 Wrap(
  children: <Widget>[*your_child*])
Match_parent,Match_parent:

 //use this as child
Container(
        height: double.infinity,

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https://gitee.com/shetj/codes/90wfc8hoqbnesdy3gvzkm14

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package com.aniketkadam.sharevideoshortcut

import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.ExperimentalCoroutinesApi
import kotlinx.coroutines.test.TestCoroutineDispatcher
import kotlinx.coroutines.test.resetMain
import kotlinx.coroutines.test.setMain
import org.junit.rules.TestRule
import org.junit.runner.Description
import org.junit.runners.model.Statement

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$ sh -c "$(curl -fsSL https://raw.github.com/robbyrussell/oh-my-zsh/master/tools/install.sh)"