自适应线性神经网络Adaptive linear network， 是神经网络的入门级别网络。

相对于感知器，采用了f（z）=z的激活函数，属于连续函数。

代价函数为LMS函数，最小均方算法，Least mean square。

实现上，采用随机梯度下降，由于更新的随机性，运行多次结果是不同的。

'''Adaline classifiercreated on 2019.9.14author: vince'''import pandas import mathimport numpy import loggingimport randomimport matplotlib.pyplot as pltfrom sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score'''Adaline classifierAttributesw: ld-array = weights after trainingl: list = number of misclassification during each iteration '''class Adaline: def __init__(self, eta = 0.001, iter_num = 500, batch_size = 1): ''' eta: float = learning rate (between 0.0 and 1.0). iter_num: int = iteration over the training dataset. batch_size: int = gradient descent batch number, if batch_size == 1, used SGD; if batch_size == 0, use BGD; else MBGD; ''' self.eta = eta; self.iter_num = iter_num; self.batch_size = batch_size; def train(self, X, Y): ''' train training data. X:{array-like}, shape=[n_samples, n_features] = Training vectors, where n_samples is the number of training samples and n_features is the number of features. Y:{array-like}, share=[n_samples] = traget values. ''' self.w = numpy.zeros(1 + X.shape[1]); self.l = numpy.zeros(self.iter_num); for iter_index in range(self.iter_num): for rand_time in range(X.shape[0]): sample_index = random.randint(0, X.shape[0] - 1); if (self.activation(X[sample_index]) == Y[sample_index]): continue; output = self.net_input(X[sample_index]); errors = Y[sample_index] - output; self.w[0] += self.eta * errors; self.w[1:] += self.eta * numpy.dot(errors, X[sample_index]); break; for sample_index in range(X.shape[0]): self.l[iter_index] += (Y[sample_index] - self.net_input(X[sample_index])) ** 2 * 0.5; logging.info("iter %s: w0(%s), w1(%s), w2(%s), l(%s)" % (iter_index, self.w[0], self.w[1], self.w[2], self.l[iter_index])); if iter_index > 1 and math.fabs(self.l[iter_index - 1] - self.l[iter_index]) < 0.0001: break; def activation(self, x): return numpy.where(self.net_input(x) >= 0.0 , 1 , -1); def net_input(self, x): return numpy.dot(x, self.w[1:]) + self.w[0]; def predict(self, x): return self.activation(x);def main(): logging.basicConfig(level = logging.INFO, format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s', datefmt = '%a, %d %b %Y %H:%M:%S'); iris = load_iris(); features = iris.data[:99, [0, 2]]; # normalization features_std = numpy.copy(features); for i in range(features.shape[1]): features_std[:, i] = (features_std[:, i] - features[:, i].mean()) / features[:, i].std(); labels = numpy.where(iris.target[:99] == 0, -1, 1); # 2/3 data from training, 1/3 data for testing train_features, test_features, train_labels, test_labels = train_test_split( features_std, labels, test_size = 0.33, random_state = 23323); logging.info("train set shape:%s" % (str(train_features.shape))); classifier = Adaline(); classifier.train(train_features, train_labels); test_predict = numpy.array([]); for feature in test_features: predict_label = classifier.predict(feature); test_predict = numpy.append(test_predict, predict_label); score = accuracy_score(test_labels, test_predict); logging.info("The accruacy score is: %s "% (str(score))); #plot x_min, x_max = train_features[:, 0].min() - 1, train_features[:, 0].max() + 1; y_min, y_max = train_features[:, 1].min() - 1, train_features[:, 1].max() + 1; plt.xlim(x_min, x_max); plt.ylim(y_min, y_max); plt.xlabel("width"); plt.ylabel("heigt"); plt.scatter(train_features[:, 0], train_features[:, 1], c = train_labels, marker = 'o', s = 10); k = - classifier.w[1] / classifier.w[2]; d = - classifier.w[0] / classifier.w[2]; plt.plot([x_min, x_max], [k * x_min + d, k * x_max + d], "go-"); plt.show(); if __name__ == "__main__": main();

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