# -*- coding: utf-8 -*-

"""

Created on Wed Jul 1 20:28:57 2020

@author: cheetah023

"""

import numpy as np

import matplotlib.pyplot as plt

import scipy.io as sci

import random as ra

#函數定義

def sigmoid(X):

return 1 /(1 + np.exp(-X))

def predict(theta1, theta2, X):

m = X.shape[0]

ones = np.ones([m,1])

X = np.column_stack([ones,X])

a2 = sigmoid(np.dot(X,theta1.T))

a2 = np.column_stack([ones,a2])

a3 = sigmoid(np.dot(a2,theta2.T))

p = np.argmax(a3,axis = 1) + 1

p = np.reshape(p,[m,1])

return p

#Part 1: Loading and Visualizing Data

data1 = sci.loadmat('ex3data1.mat')

#print(data) #data是個字典類型

X = data1['X']

y = data1['y']

#print('X',X.shape)

#print('y',y.shape)

#Part 2: Loading Pameters

data2 = sci.loadmat('ex3weights.mat')

#print(data2.keys())

theta1 = data2['Theta1']

theta2 = data2['Theta2']

#print('theta1',theta1.shape)

#print('theta2',theta2.shape)

#Part 3: Implement Predict

p = predict(theta1, theta2, X)

#temp里面的值是True(=1)和False(=0)

temp = (p==y)

prob = np.mean(temp)

print('Training Set Accuracy:',prob)

總結

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