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摘要：# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,decomposition def load_data(): ''' 加载用于降维的数据 ''' # 使用 scikit-learn 自带的 iris ... 阅读全文

摘要：# -*- coding: utf-8 -*- import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,decomposition def load_data(): ''' 加载用于降维的数据 ''' # 使用 scikit-learn 自带的 iris ... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import neighbors, datasets from sklearn.model_selection import train_test_split def create_regression_data(n): ''' 创建回归模型使用的数... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import neighbors, datasets from sklearn.model_selection import train_test_split def load_classification_data(): # 使用 scikit-learn... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,naive_bayes from sklearn.model_selection import train_test_split # 加载 scikit-learn 自带的 digits 数据集 def load_data(): ... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets,naive_bayes from sklearn.model_selection import train_test_split # 加载 scikit-learn 自带的 digits 数据集 def load_data(): ... 阅读全文

摘要：import matplotlib.pyplot as plt from sklearn import datasets,naive_bayes from sklearn.model_selection import train_test_split # 加载 scikit-learn 自带的 digits 数据集 def load_data(): ''' 加载用于分类问题... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegre... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegre... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn.model_selection import train_test_split from sklearn import datasets... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model from sklearn.model_selection import tr... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model from sklearn.model_selection import tr... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diab... 阅读全文

摘要：import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diab... 阅读全文

摘要：import numpy as np from sklearn import datasets,linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diabetes() return train_test_spli... 阅读全文

摘要：import numpy as np def loadDataSet(): dataMat = [] labelMat = [] fr = open('D:\\LearningResource\\machinelearninginaction\\Ch05\\testSet.txt') for line in fr.readlines(): lin... 阅读全文

摘要：一、熵权法介绍 熵最先由申农引入信息论，目前已经在工程技术、社会经济等领域得到了非常广泛的应用。 熵权法的基本思路是根据各个特征和它对应的值的变异性的大小来确定客观权重。 一般来说，若某个特征的信息熵越小，表明该特征的值得变异（对整体的影响）程度越大，提供的信息量越多，在综合评价中所能起到 的作用也 阅读全文

摘要：import matplotlib import numpy as np import matplotlib.pyplot as plt n = 1000 xcord0 = [] ycord0 = [] xcord1 = [] ycord1 = [] markers =[] colors =[] for i in range(n): [r0,r1] = np.random.stan... 阅读全文

摘要：import numpy as np def loadDataSet(): postingList=[['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'], ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'], ... 阅读全文