import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_classification

class logistic_regression():
? ? def __init__(self):
? ? ? ? pass
? ? def sigmoid(self, x):
? ? ? ? z = 1 /(1 + np.exp(-x))
? ? ? ? return z

? ? def initialize_params(self, dims):
? ? ? ? W = np.zeros((dims, 1))
? ? ? ? b = 0
? ? ? ? return W, b

? ? def logistic(self, X, y, W, b):
? ? ? ? num_train = X.shape[0]
? ? ? ? num_feature = X.shape[1]
? ? ? ? a = self.sigmoid(np.dot(X, W) + b)
? ? ? ? cost = -1 / num_train * np.sum(y * np.log(a) + (1 - y) * np.log(1 - a))
? ? ? ? dW = np.dot(X.T, (a - y)) / num_train
? ? ? ? db = np.sum(a - y) / num_train
? ? ? ? cost = np.squeeze(cost)#[]列向量，易于plot
? ? ? ? return a, cost, dW, db

? ? def logistic_train(self, X, y, learning_rate, epochs):
? ? ? ? W, b = self.initialize_params(X.shape[1])
? ? ? ? cost_list = []
? ? ? ? for i in range(epochs):
? ? ? ? ? ? a, cost, dW, db = self.logistic(X, y, W, b)
? ? ? ? ? ? W = W - learning_rate * dW
? ? ? ? ? ? b = b - learning_rate * db
? ? ? ? ? ? if i % 100 == 0:
? ? ? ? ? ? ? ? cost_list.append(cost)
? ? ? ? ? ? if i % 100 == 0:
? ? ? ? ? ? ? ? print('epoch %d cost %f' % (i, cost))
? ? ? ? params = {
? ? ? ? ? ? 'W': W,
? ? ? ? ? ? 'b': b
? ? ? ? }
? ? ? ? grads = {
? ? ? ? ? ? 'dW': dW,
? ? ? ? ? ? 'db': db
? ? ? ? }
? ? ? ? return cost_list, params, grads

? ? def predict(self, X, params):
? ? ? ? y_prediction = self.sigmoid(np.dot(X, params['W']) + params['b'])
? ? ? ? #二分類
? ? ? ? for i in range(len(y_prediction)):
? ? ? ? ? ? if y_prediction[i] > 0.5:
? ? ? ? ? ? ? ? y_prediction[i] = 1
? ? ? ? ? ? else:
? ? ? ? ? ? ? ? y_prediction[i] = 0
? ? ? ? return y_prediction

? ? #精確度計(jì)算
? ? def accuracy(self, y_test, y_pred):
? ? ? ? correct_count = 0
? ? ? ? for i in range(len(y_test)):
? ? ? ? ? ? for j in range(len(y_pred)):
? ? ? ? ? ? ? ? if y_test[i] == y_pred[j] and i == j:
? ? ? ? ? ? ? ? ? ? correct_count += 1
? ? ? ? accuracy_score = correct_count / len(y_test)
? ? ? ? return accuracy_score

? ? #創(chuàng)建數(shù)據(jù)
? ? def create_data(self):
? ? ? ? X, labels = make_classification(n_samples=100, n_features=2, n_redundant=0, n_informative=2)
? ? ? ? labels = labels.reshape((-1, 1))
? ? ? ? offset = int(X.shape[0] * 0.9)
? ? ? ? #訓(xùn)練集與測(cè)試集的劃分
? ? ? ? X_train, y_train = X[:offset], labels[:offset]
? ? ? ? X_test, y_test = X[offset:], labels[offset:]
? ? ? ? return X_train, y_train, X_test, y_test

? ? #畫(huà)圖函數(shù)
? ? def plot_logistic(self, X_train, y_train, params):
? ? ? ? n = X_train.shape[0]
? ? ? ? xcord1 = []
? ? ? ? ycord1 = []
? ? ? ? xcord2 = []
? ? ? ? ycord2 = []
? ? ? ? for i in range(n):
? ? ? ? ? ? if y_train[i] == 1:#1類
? ? ? ? ? ? ? ? xcord1.append(X_train[i][0])
? ? ? ? ? ? ? ? ycord1.append(X_train[i][1])
? ? ? ? ? ? else:#0類
? ? ? ? ? ? ? ? xcord2.append(X_train[i][0])
? ? ? ? ? ? ? ? ycord2.append(X_train[i][1])
? ? ? ? fig = plt.figure()
? ? ? ? ax = fig.add_subplot(111)
? ? ? ? ax.scatter(xcord1, ycord1, s=32, c='red')
? ? ? ? ax.scatter(xcord2, ycord2, s=32, c='green')#畫(huà)點(diǎn)
? ? ? ? x = np.arange(-1.5, 3, 0.1)
? ? ? ? y = (-params['b'] - params['W'][0] * x) / params['W'][1]#畫(huà)二分類直線
? ? ? ? ax.plot(x, y)
? ? ? ? plt.xlabel('X1')
? ? ? ? plt.ylabel('X2')
? ? ? ? plt.show()


if __name__ == "__main__":
? ? model = logistic_regression()
? ? X_train, y_train, X_test, y_test = model.create_data()
? ? print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
? ? # (90, 2)(90, 1)(10, 2)(10, 1)
? ? #訓(xùn)練模型
? ? cost_list, params, grads = model.logistic_train(X_train, y_train, 0.01, 1000)
? ? print(params)
? ? #計(jì)算精確度
? ? y_train_pred = model.predict(X_train, params)
? ? accuracy_score_train = model.accuracy(y_train, y_train_pred)
? ? print('train accuracy is:', accuracy_score_train)
? ? y_test_pred = model.predict(X_test, params)
? ? accuracy_score_test = model.accuracy(y_test, y_test_pred)
? ? print('test accuracy is:', accuracy_score_test)
? ? model.plot_logistic(X_train, y_train, params)