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
0916643858
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()
