56 lines
1.5 KiB
Python
56 lines
1.5 KiB
Python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from k_means import KMeans
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data = pd.read_csv('../data/iris.csv')
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iris_types = ['SETOSA','VERSICOLOR','VIRGINICA']
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x_axis = 'petal_length'
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y_axis = 'petal_width'
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plt.figure(figsize=(12,5))
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plt.subplot(1,2,1)
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for iris_type in iris_types:
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plt.scatter(data[x_axis][data['class']==iris_type],data[y_axis][data['class']==iris_type],label = iris_type)
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plt.title('label known')
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plt.legend()
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plt.subplot(1,2,2)
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plt.scatter(data[x_axis][:],data[y_axis][:])
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plt.title('label unknown')
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plt.show()
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num_examples = data.shape[0]
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x_train = data[[x_axis,y_axis]].values.reshape(num_examples,2)
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#指定好训练所需的参数
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num_clusters = 3
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max_iteritions = 50
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k_means = KMeans(x_train,num_clusters)
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centroids,closest_centroids_ids = k_means.train(max_iteritions)
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# 对比结果
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plt.figure(figsize=(12,5))
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plt.subplot(1,2,1)
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for iris_type in iris_types:
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plt.scatter(data[x_axis][data['class']==iris_type],data[y_axis][data['class']==iris_type],label = iris_type)
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plt.title('label known')
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plt.legend()
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plt.subplot(1,2,2)
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for centroid_id, centroid in enumerate(centroids):
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current_examples_index = (closest_centroids_ids == centroid_id).flatten()
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plt.scatter(data[x_axis][current_examples_index],data[y_axis][current_examples_index],label = centroid_id)
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for centroid_id, centroid in enumerate(centroids):
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plt.scatter(centroid[0],centroid[1],c='black',marker = 'x')
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plt.legend()
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plt.title('label kmeans')
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plt.show()
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