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%matplotlib inline

"""
Dimensionality Reduction takes a highly dimensional data source and reduces its size through
related features.
"""


    

    
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'\nDimensionality Reduction takes a highly dimensional data source and reduces its size through\nrelated features.\n'

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import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris


    

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data = load_iris()
y = data.target
X = data.data
pca = PCA(n_components=2)
reduced_X = pca.fit_transform(X)


    

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red_x, red_y = [], []
blue_x, blue_y = [], []
green_x, green_y = [], []

for i in range(len(reduced_X)):
    if y[i] == 0:
        red_x.append(reduced_X[i][0])
        red_y.append(reduced_X[i][1])
    elif y[i] == 1:
        blue_x.append(reduced_X[i][0])
        blue_y.append(reduced_X[i][1])
    else:
        green_x.append(reduced_X[i][0])
        green_y.append(reduced_X[i][1])
        
plt.scatter(red_x, red_y, c='r', marker='x')
plt.scatter(blue_x, blue_y, c='b', marker='D')
plt.scatter(green_x, green_y, c='g', marker='.')
plt.show()


    

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