#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2018/4/4 19:22 # @Author : HJH # @Site : # @File : mul_logistics.py # @Software: PyCharm import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from numpy import * from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score import pickle #从文件中加载数据:特征X,标签label def loadDataSet(): digits=load_iris() X_train = digits.data[:-20,:] y_train = digits.target[:-20] # print(y_train.shape) X_test = digits.data[-20:,:] y_test =digits.target[-20:] return X_train,y_train,X_test,y_test def plot(): fig = plt.figure() data = load_iris() x_index = 0 y_index = 1 colors = ['blue', 'red','green'] # plt.subplot(211) for label, color in zip(range(len(data.target_names)), colors): plt.scatter(data.data[data.target == label, x_index], data.data[data.target == label, y_index], label=data.target_names[label], c=color) plt.xlabel(data.feature_names[x_index]) plt.ylabel(data.feature_names[y_index]) plt.legend(loc='upper left') plt.show() def main(): X_train, y_train, X_test, y_test=loadDataSet() lr_model=LogisticRegression() lr_model.fit(X_train,y_train) y_pred=lr_model.predict(X_test) print(accuracy_score(y_test,y_pred)) with open('./log.pkl','wb') as f: pickle.dump(lr_model,f) # digits = load_iris() # with open('./log.pkl','rb') as f: # model=pickle.load(f) # random_index=np.random.randint(0,100,5) # random_samples=digits.data[random_index,:] # random_targets=digits.target[random_index] # random_pred=model.predict(random_samples) # print(random_pred) # print(random_targets) if __name__=='__main__': main() plot()
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