import matplotlib.pyplot as plt | |
from sklearn import datasets | |
from sklearn.decomposition import TruncatedSVD | |
# Enable saving plot to png file without UI | |
plt.switch_backend('agg') | |
iris = datasets.load_iris() | |
X = iris.data | |
y = iris.target | |
# Visualize result using SVD | |
svd = TruncatedSVD(n_components=2) | |
X_reduced = svd.fit_transform(X) | |
# Get min and max sizes for coordinate axis | |
x_min, x_max = X_reduced[:, 0].min() - .5, X_reduced[:, 0].max() + .5 | |
y_min, y_max = X_reduced[:, 1].min() - .5, X_reduced[:, 1].max() + .5 | |
# Initialize scatter plot with x and y axis values | |
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=y, s=25) | |
plt.axis([float(x_min), float(x_max), float(y_min), float(y_max)]) | |
plt.savefig("iris_plot.png") | |
plt.show() |
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