esmitt

#include <iostream.h> //example of operator overloading

template<class T>
class Stack {
public:
   Stack(int n); 
   Stack(Stack<T>& s); //copy constructor
   ~Stack() {delete [] stackPtr;}   // destructor
   Stack<T> operator +  (const Stack<T>& s2) const; //overloading +
   Stack<T>& operator = (const Stack<T>& s); //overloading assignment     

esmitt

import sys
import pandas as pd

filename = sys.argv[1]
filename = filename[:-4]
read_file = pd.read_csv (filename + ".csv")
read_file.to_excel (filename + ".xlsx", index = None, header=True)

# run as 
# python csv_to_xlsx.py %1

esmitt

from tensorflow.keras import Model
from tensorflow.keras.layers import Convolution2D
from tensorflow.keras.layers import MaxPool2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Input
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import concatenate
from tensorflow.keras.optimizers import Adam

esmitt

#include <windows.h>
#include <GLFW/glfw3.h>
#include <iostream>

// change this to int main() to allow the console
int WINAPI WinMain(HINSTANCE hInstance, HINSTANCE hPrevInstance, char*, int nShowCmd)
{
  GLFWwindow* window;
  int windowSizeW = 640, windowSizeH = 480;
  // initialize the library

esmitt

#include <iostream>
#include <string>
#include <sstream>

template <typename T>
std::string to_string_with_precision(const T a_value, const int n = 4)
{
  std::ostringstream out;
  out.precision(n);
  out << std::fixed << a_value;

esmitt

    import scipy.io as scio
    from typing import Any, Dict
    import numpy as np
    
    
    def load_matfile(filename: str) -> Dict:
        def parse_mat(element: Any):
            # lists (1D cell arrays usually) or numpy arrays as well
            if element.__class__ == np.ndarray and element.dtype == np.object_ and len(element.shape) > 0:
                return [parse_mat(entry) for entry in element]

esmitt

# first, install requests and matplotlib (pip install requests matplotlib) 
from urllib.request import urlopen
from PIL import Image
import matplotlib.pyplot as plt
import requests

api_url_pokemon = 'https://pokeapi.co/api/v2/pokemon/pikachu'
result = requests.get(api_url_pokemon)
if result.status_code == 200:
    pokemon_data = result.json()

esmitt

from scipy.stats import describe
import numpy as np

# arr_values is a numpy array
def print_stats(arr_values: np.array) -> None:
  stats = describe(arr_values)
  print(f'min: {stats.minmax[0]:.5f}, max: {stats.minmax[1]:.4f}')
  print(f'mean: {stats.mean:.5f}')
  print(f'standard: {np.std(arr_values):.5f}')
  print(f'variance: {stats.variance:.5f}')

esmitt

@echo off

REM Remove files generated by compiler in this directory and all subdirectories.
REM Essential release files are kept.

echo Removing "*.csproj.user" files...
for /f "delims==" %%i in ('dir /b /on /s "%~p0*.csproj.user"') do del "%%i" /f /q
echo.

echo Removing "*.exe.config" files...

esmitt

from sklearn.metrics import roc_auc_score, roc_curve

def plot_roc(name: str, labels: numpy.ndarray, predictions: numpy.ndarray, **kwargs) -> ():
  fp, tp, _ = roc_curve(labels, predictions)
  auc_roc = roc_auc_score(labels, predictions)
  plt.plot(100*fp, 100*tp, label=name + " (" + str(round(auc_roc, 3)) + ")", 
           linewidth=2, **kwargs)
  plt.xlabel('False positives [%]')
  plt.ylabel('True positives [%]')
  plt.title('ROC curve')