View stack_template.cpp
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#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 |
View csv_to_xlsx.py
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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 |
View starting_functionalCNN.py
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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 |
View transparency_glfw.cpp
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#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 |
View tostring.cpp
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#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; |
View load_mat.py
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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] |
View pokemon_jupyter.py
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# 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() |
View statistic-list.py
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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}') |
View cleaning-csharp.bat
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@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... |
View plot-ROC.py
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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') |
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