import numpy as np import keras as k from keras.layers import Input, Dense from keras.models import Model # xData = np.full((4, 2), 0, dtype=np.float32) yTrainData = np.full((4, 2), 0, dtype=np.float32) # xData[0][0] = 0 xData[0][1] = 0 xData[1][0] = 1 xData[1][1] = 0 xData[2][0] = 0 xData[2][1] = 1 xData[3][0] = 1 xData[3][1] = 1 yTrainData[0][0] = 1 yTrainData[0][1] = 0 yTrainData[1][0] = 0 yTrainData[1][1] = 1 yTrainData[2][0] = 0 yTrainData[2][1] = 1 yTrainData[3][0] = 1 yTrainData[3][1] = 0 inputs = Input(shape=(2,)) x = Dense(2, input_dim=2, activation='sigmoid')(inputs) predictions = De

data = [[0.697, 0.460, 1], [0.774, 0.376, 1], [0.634, 0.264, 1], [0.608, 0.318, 1], [0.556, 0.215, 1], [0.430, 0.237, 1], [0.481, 0.149, 1], [0.437, 0.211, 1], [0.666, 0.091, 0], [0.243, 0.267, 0], [0.245, 0.057, 0], [0.343, 0.099, 0], [0.639, 0.161, 0], [0.657, 0.198, 0], [0.360, 0.370, 0], [0.593, 0.042, 0], [0.719, 0.103, 0], [0.359, 0.188, 0], [0.339, 0.241, 0], [0.282, 0.257, 0], [0.748, 0.232, 0], [0.714, 0.346, 1], [0.483, 0.312, 1], [0.478, 0.437, 1], [0.525, 0.369, 1], [0.751, 0.489, 1], [0.532, 0.472, 1], [0.473, 0.376, 1], [0.725, 0.445, 1], [0.446, 0.459, 1]]

https://www.jianshu.com/p/164869f8c489

def getDataSet(): """ get watermelon data set 3.0 alpha. :return: 编码好的数据集以及特征的字典。 """ dataSet = [ ['青绿', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', 0.697, 0.460, 1], ['乌黑', '蜷缩', '沉闷', '清晰', '凹陷', '硬滑', 0.774, 0.376, 1], ['乌黑', '蜷缩', '浊响', '清晰', '凹陷', '硬滑', 0.634, 0.264, 1], ['青绿', '蜷缩', '沉闷', '

多元线性回归

云计算二

云计算三

import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classification_reportdensity=np.array([0.697,0.774,0.634,0.608,0.556,0.430,0.481,0.437,0.666,0.243,0.245,0.343,0.639,0.657,0.360,0.593,0.719]).reshape(-1,1)sugar_rate=np.array([0.460,0.376,0.264,0.318,0.215,0.237,0.149,0.211,0.091,0.267,0.057,0.099,0.161,0.198,0.370,0.042,0.103]).reshape(-1,1)xtrain=np.hstack((density,sugar_rate))xtrain=np.hstack((np.ones([density.shape[0],1]),xtrain))ytrain=np.array([1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0]).reshape(-1,1)xtrain,xtest,ytrain,ytest=train_test_split(xtrai

https://blog.csdn.net/weixin_30932215/article/details/101857873

#include <iostream> #include<string> #include<vector> #include <fstream> #include <sstream> #include<cmath> using namespace std; struct DataPoint { vector<double> Data; int cluster; }; static double DataMap[1000][1000];//保存两两聚类之间的距离 int MaxClu; //最终获得聚类数量 int p; //存储最初的聚类的个数 vector<DataPoint> DataBase; //保存所有数据 vector<vector<DataPoint> > CluData; //保存所有簇类数据 void GetData() { DataPoint a; double d; int num; cout << "请输入数据项的个

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