wehub-resource-sync
47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
from sklearn import datasets
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import matplotlib.pyplot as plt
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import matplotlib.cm as cmx
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import matplotlib.colors as colors
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import numpy as np
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from mlfromscratch.unsupervised_learning import PCA
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def main():
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# Demo of how to reduce the dimensionality of the data to two dimension
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# and plot the results.
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# Load the dataset
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data = datasets.load_digits()
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X = data.data
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y = data.target
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# Project the data onto the 2 primary principal components
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X_trans = PCA().transform(X, 2)
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x1 = X_trans[:, 0]
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x2 = X_trans[:, 1]
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cmap = plt.get_cmap('viridis')
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colors = [cmap(i) for i in np.linspace(0, 1, len(np.unique(y)))]
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class_distr = []
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# Plot the different class distributions
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for i, l in enumerate(np.unique(y)):
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_x1 = x1[y == l]
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_x2 = x2[y == l]
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_y = y[y == l]
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class_distr.append(plt.scatter(_x1, _x2, color=colors[i]))
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# Add a legend
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plt.legend(class_distr, y, loc=1)
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# Axis labels
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plt.suptitle("PCA Dimensionality Reduction")
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plt.title("Digit Dataset")
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plt.xlabel('Principal Component 1')
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plt.ylabel('Principal Component 2')
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plt.show()
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if __name__ == "__main__":
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main() |