标签:use hose called open etc random ace star tor
The k-means algorithm captures the insight that each point in a cluster should be near to the center of that cluster. It works like this: first we choose k, the number of clusters we want to find in the data. Then, the centers of those k clusters, called centroids, are initialized in some fashion, (discussed later).
#!/usr/bin/python """ Skeleton code for k-means clustering mini-project. """ import pickle import numpy import matplotlib.pyplot as plt import sys sys.path.append("../tools/") from feature_format import featureFormat, targetFeatureSplit from sklearn.cluster import KMeans def Draw(pred, features, poi, mark_poi=False, name="image.png", f1_name="feature 1", f2_name="feature 2"): """ some plotting code designed to help you visualize your clusters """ ### plot each cluster with a different color--add more colors for ### drawing more than five clusters colors = ["b", "c", "k", "m", "g"] for ii, pp in enumerate(pred): plt.scatter(features[ii][0], features[ii][1], color = colors[pred[ii]]) ### if you like, place red stars over points that are POIs (just for funsies) if mark_poi: for ii, pp in enumerate(pred): if poi[ii]: plt.scatter(features[ii][0], features[ii][1], color="r", marker="*") plt.xlabel(f1_name) plt.ylabel(f2_name) plt.savefig(name) plt.show() ### load in the dict of dicts containing all the data on each person in the dataset data_dict = pickle.load( open("../final_project/final_project_dataset.pkl", "r") ) ### there‘s an outlier--remove it! data_dict.pop("TOTAL", 0) ### the input features we want to use ### can be any key in the person-level dictionary (salary, director_fees, etc.) feature_1 = "salary" feature_2 = "exercised_stock_options" poi = "poi" features_list = [poi, feature_1, feature_2] data = featureFormat(data_dict, features_list ) poi, finance_features = targetFeatureSplit( data ) ### in the "clustering with 3 features" part of the mini-project, ### you‘ll want to change this line to ### for f1, f2, _ in finance_features: ### (as it‘s currently written, the line below assumes 2 features)
for f1, f2 in finance_features: plt.scatter( f1, f2 ) plt.show() ### cluster here; create predictions of the cluster labels ### for the data and store them to a list called pred kmeans = KMeans(n_clusters=2, random_state=0).fit(finance_features) pred = kmeans.predict(finance_features) ### rename the "name" parameter when you change the number of features ### so that the figure gets saved to a different file try: Draw(pred, finance_features, poi, mark_poi=False, name="clusters.pdf", f1_name=feature_1, f2_name=feature_2) except NameError: print "no predictions object named pred found, no clusters to plot"
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After:
Some tools: https://www.naftaliharris.com/blog/visualizing-k-means-clustering/
标签:use hose called open etc random ace star tor
原文地址:https://www.cnblogs.com/Answer1215/p/13332757.html