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adaline.py
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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.colors import ListedColormap
class AdalineGD(object):
def __init__(self,eta=0.0001,n_iter=50):
self.eta=eta
self.n_iter=n_iter
def fit(self,X,y):
self.w_ = np.zeros(1+X.shape[1])
self.cost_ = []
#print("w_ is: ",self.w_)
print("w_[0] is: ",self.w_[0])
for i in range(self.n_iter):
output = self.net_input(X)
print("X is :",X)
errors = (y-output)
print("errors are : ",errors)
self.w_[1:] = self.w_[1:] + self.eta*X.T.dot(errors)
self.w_[0] = self.w_[0] + self.eta*errors.sum()
print("w_[0] is: ",self.w_[0])
print("w_[1:] is: ",self.w_[1:])
cost = (errors**2).sum()/2.0
self.cost_.append(cost)
#print("cost_ is : ",self.cost_)
return self
def net_input(self,X):
return np.dot(X,self.w_[1:]+self.w_[0])
def activation(self,X):
return self.net_input(X)
def predict(self,X):
return np.where(self.activation(X) >= 0.0,1,-1)
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=None)
df.tail()
y = df.iloc[0:100 , 4].values
y = np.where(y=='Iris-setosa' , -1,1)
X = df.iloc[0:100,[0,2]].values
#print("y is :",y)
#print("X is :",X)
#print("df is :",df)
fig , ax = plt.subplots(nrows=1,ncols=2,figsize=(8,4))
ada1 = AdalineGD(n_iter=10,eta=0.01).fit(X,y)
ax[0].plot(range(1,len(ada1.cost_) +1), np.log10(ada1.cost_), marker='o')
ax[0].set_xlabel('Epochs')
ax[0].set_ylabel('log(sum-squared-errors)')
ax[0].set_title('Adaline Learning rate : 0.01')
ada2 = AdalineGD(n_iter=10,eta=0.0001).fit(X,y)
ax[1].plot(range(1,len(ada2.cost_) +1), ada2.cost_, marker='o')
ax[1].set_xlabel('Epochs')
ax[1].set_ylabel('sum-squared-errors')
ax[1].set_title('Adaline Learning rate : 0.0001')
plt.show()
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()
#plot_decision_region
def plot_decision_regions(X , y , classifier , resolution=0.02 ):
#setup marker
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 sample
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)
ada = AdalineGD(n_iter=15 , eta=0.01)
ada.fit(X_std,y)
plot_decision_regions(X_std,y,classifier=ada)
plt.title('Adaline Gradient Descent')
plt.xlabel('sepal length [standardized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.show()
plt.plot(range(1,len(ada.cost_)+1) , ada.cost_ , marker='o' )
plt.xlabel('Epochs')
plt.ylabel('sum-squared-errors')
plt.show()