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How to modify Adaline Stochastic gradient descent

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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
 
 
 

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()








 

 

Adaline Stochastic gradient descent.pdf

Python Stochastic gradient descent.py

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