bro's coding

# 3 X 1080 X 1920 -> 1080 X 1920 X 3(moveaxis) X_train=np.moveaxis(X_train.reshape(-1,3,32,32),1,-1)

# score (np.argmax(pred_y,axis=1)==iris.target).mean()

import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_breast_cancer cancer = load_breast_cancer() X=cancer.data y=cancer.target plt.scatter(X[:,0],X[:,2]) plt.axis('equal') np.corrcoef(X[:,0],X[:,2]) array([[1. , 0.99785528], [0.99785528, 1. ]]) mat=np.corrcoef(X.T) mat plt.imshow(mat,vmin=-1,vmax=1,cmap='bwr') plt.colorbar() idx = [0, 2, 3, 12, 13, 20, 22, 23, 1, ..

y=mnist.target.copy() y=np.where(y==9,1,0) ''' if(y==9): y=1 else: y=0 '''

a = np.ones([4,3]) a ''' array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]) ''' a+[1,2,3] ''' [2,3,4] [2,3,4] [2,3,4] [2,3,4] ''' https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html Broadcasting — NumPy v1.17 Manual Broadcasting Note See this article for illustrations of broadcasting concepts. The term broadcasting describes how numpy treats arrays with different shapes d..

a = np.arange(12).reshape(4,3) a ''' array([[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8], [ 9, 10, 11]]) ''' 1/a ''' array([[ inf, 1. , 0.5 ], [0.33333333, 0.25 , 0.2 ], [0.16666667, 0.14285714, 0.125 ], [0.11111111, 0.1 , 0.09090909]]) '''

import numpy as np a = np.array([1,2,3,4,5]) a+1 [2,3,4,5,6] a*4 [4,8,12,16,20]

import numpy as np iris_labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',', converters={4: lambda s: iris_labels.index(s.decode())}) # iris = np.loadtxt('iris.csv', skiprows=1, delimiter=',', # converters={4: lambda s: labels.index(s)}, encoding='utf-8') # latin1, ascii, utf-8, cp949