SelectFromModel(estimator=LinearSVC(penalty='l1'))), ("classification", RandomForestClassifier())]) clf.fit(x, y) # np.random.RandomState(1) # 1 为种子 # RandomState.rand(d0, d1, ..., dn) #The dimensions of the returned array, should all be positive. If no argument is given a single Python float is returned. # 多层感知机分类 from sklearn.neural_network import MLPClassifier # 利用了BP x = [[0, 0], [1, 1]] # 使用 SGD 或 Adam ,训练过程支持在线模式和小批量学习模式 y = [0, 1] # 小数据集下,lbfgs运行更快,表现更好;大数据集,adam更好 clf = MLPClassifier( solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1) # alpha:L2 penalty (regularization term) parameter clf.fit(x, y) clf.predict([[2, 2], [-1, -2]]) # 区域中浣熊脸的图片分割 from time import time from sklearn.cluster import spectral_clustering import matplotlib.pyplot as plt import numpy as np import scipy as sp from sklearn.feature_extraction import image from scipy.misc import face face = face( gray=True) # face.shape=(768, 1024), type(face)= <class 'numpy.ndarray'> face = sp.misc.imresize( face, 0.1) / 255 #face.shape=(76,102),缩小当前尺寸的0.1,除以255,将数组归一化 graph = image.img_to_graph( face