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 March 14th, 2019, 08:02 AM #1 Newbie   Joined: Apr 2018 From: Taiwan Posts: 4 Thanks: 0 How to modify Adaline Stochastic gradient descent Dear May I know how to modify my own Python programming so that I will get the same picture as refer to the attached file - Adaline Stochastic gradient descent (I am using the Anaconda Python 3.7) Prayerfully Tron Orino Yeong tcynotebook@yahoo.com 0916643858 Code:  from matplotlib.colors import ListedColormap import matplotlib.pyplot as plt import numpy as np from numpy.random import seed import pandas as pd # Stochastic Gradient Descent class SGD(object): def __init__(self, rate = 0.01, niter = 10, shuffle=True, random_state=None): self.rate = rate self.niter = niter self.weight_initialized = False # If True, Shuffles training data every epoch self.shuffle = shuffle # Set random state for shuffling and initializing the weights. if random_state: seed(random_state) def fit(self, X, y): """Fit training data X : Training vectors, X.shape : [#samples, #features] y : Target values, y.shape : [#samples] """ # weights self.initialize_weights(X.shape[1]) # Cost function self.cost = [] for i in range(self.niter): if self.shuffle: X, y = self.shuffle_set(X, y) cost = [] for xi, target in zip(X, y): cost.append(self.update_weights(xi, target)) avg_cost = sum(cost)/len(y) self.cost.append(avg_cost) return self def partial_fit(self, X, y): """Fit training data without reinitializing the weights""" if not self.weight_initialized: self.initialize_weights(X.shape[1]) if y.ravel().shape[0] > 1: for xi, target in zip(X, y): self.update_weights(xi, target) else: self.up return self def shuffle_set(self, X, y): """Shuffle training data""" r = np.random.permutation(len(y)) return X[r], y[r] def initialize_weights(self, m): """Initialize weights to zeros""" self.weight = np.zeros(1 + m) self.weight_initialized = True def update_weights(self, xi, target): """Apply SGD learning rule to update the weights""" output = self.net_input(xi) error = (target - output) self.weight[1:] += self.rate * xi.dot(error) self.weight[0] += self.rate * error cost = 0.5 * error**2 return cost def net_input(self, X): """Calculate net input""" return np.dot(X, self.weight[1:]) + self.weight[0] def activation(self, X): """Compute linear activation""" return self.net_input(X) def predict(self, X): """Return class label after unit step""" return np.where(self.activation(X) >= 0.0, 1, -1) def plot_decision_regions(X, y, classifier, resolution=0.02): # setup marker generator and color map markers = ('s', 'x', 'o', '^', 'v') colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan') cmap = ListedColormap(colors[:len(np.unique(y))]) # plot the decision surface x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1 x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution), np.arange(x2_min, x2_max, resolution)) Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T) Z = Z.reshape(xx1.shape) plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap) plt.xlim(xx1.min(), xx1.max()) plt.ylim(xx2.min(), xx2.max()) # plot class samples for idx, cl in enumerate(np.unique(y)): plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1], alpha=0.8, c=cmap(idx), marker=markers[idx], label=cl) df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None) y = df.iloc[0:100, 4].values y = np.where(y == 'Iris-setosa', -1, 1) X = df.iloc[0:100, [0, 2]].values # standardize X_std = np.copy(X) X_std[:,0] = (X[:,0] - X[:,0].mean()) / X[:,0].std() X_std[:,1] = (X[:,1] - X[:,1].mean()) / X[:,1].std() sgd1 = SGD(niter=100, rate=0.01, random_state=1) sgd2 = SGD(niter=50, rate=0.01, random_state=1) sgd3 = SGD(niter=10, rate=0.01, random_state=1) sgd1.fit(X_std, y) sgd2.fit(X_std, y) sgd3.fit(X_std, y) plt.plot(range(1, len(sgd1.cost) + 1), sgd1.cost, marker='o', linestyle='oo', label='batch=1') plt.plot(range(1, len(sgd2.cost_) + 1), np.array(sgd2.cost_) / len(y_train), marker='o', linestyle='--', label='batch=2') plt.plot(range(1, len(sgd3.cost_) + 1), np.array(sgd3.cost_) / len(y_train), marker='o', linestyle='xx', label='batch=3') plt.xlabel('Epochs') plt.ylabel('Average Cost') plt.show() https://www.scienceforums.net/topic/...omment-1097272 Last edited by vokoyo; March 14th, 2019 at 08:06 AM.

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