Principle Component Analysis (PCA)

 Principle Component Analysis (PCA)

This is Machine Learning based algorithm lies under the Unsupervised Learning. This is the simplest code using python

import matplotlib.pyplot as plt
from sklearn import datasets
import numpy as np
iris=datasets.load_iris()
data=np.array(iris.data[:10,:2])
xdata=data[:,0]
ydata=data[:,1]
n=len(xdata)
print(n)
x=np.mean(xdata)
y=np.mean(ydata)
c=np.cov(xdata,ydata)
values, vector = np.linalg.eig(c)
values[::-1].sort() 
vector_subset = vector[:,0:2]
values_subset = np.dot(vector_subset.transpose(),values.transpose()).transpose()
print("Vector Subset is ",vector_subset)
print("Values Subset ",values_subset)
plt.plot(values,vector[:,0])
plt.title('After PCA')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()
plt.plot(xdata,ydata)
plt.title('Before PCA')
plt.xlabel('PC1')
plt.ylabel('PC2')
plt.legend()
plt.show()