add all scripts/tools

1 files changed, 76 insertions, 0 deletions

diff --git a/cluster/kMeans.py b/cluster/kMeans.py
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+++ b/cluster/kMeans.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+#
+# Credit: Machine Learning in Action: Chapter 10
+#
+# Aaron LI
+# 2015/06/23
+#
+
+"""
+k-means clustering algorithm
+"""
+
+
+import numpy as np
+
+
+def loadDataSet(fileName):
+    dataMat = []
+    fr = open(fileName)
+    for line in fr.readlines():
+        curLine = line.strip().split('\t')
+        fltLine = list(map(float, curLine))
+        dataMat.append(fltLine)
+    return np.array(dataMat)
+
+
+def distEclud(vecA, vecB):
+    return np.sqrt(np.sum(np.power(vecA - vecB, 2)))
+
+
+def randCent(dataSet, k):
+    n = np.shape(dataSet)[1]
+    centroids = np.zeros((k, n))
+    for j in range(n):
+        minJ = np.min(dataSet[:, j])
+        rangeJ = float(np.max(dataSet[:, j]) - minJ)
+        centroids[:, j] = minJ + rangeJ * np.random.rand(k)
+    return centroids
+
+
+def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
+    m = np.shape(dataSet)[0]
+    clusterAssment = np.zeros((m, 2))
+    centroids = createCent(dataSet, k)
+    clusterChanged = True
+    iterations = 0
+    while clusterChanged:
+        clusterChanged = False
+        iterations += 1
+        for i in range(m):
+            minDist = np.inf
+            minIndex = -1
+            # to find the nearest centroid
+            for j in range(k):
+                distJI = distMeas(centroids[j, :], dataSet[i, :])
+                if distJI < minDist:
+                    minDist = distJI
+                    minIndex = j
+            if clusterAssment[i, 0] != minIndex:
+                clusterChanged = True
+            clusterAssment[i, :] = minIndex, minDist**2
+        #print(centroids)
+        for cent in range(k):
+            ptsInClust = dataSet[np.nonzero(clusterAssment[:, 0] == cent)]
+            centroids[cent, :] = np.mean(ptsInClust, axis=0)
+    result = {
+            'k': k,
+            'centroids': centroids,
+            'labels': clusterAssment[:, 0].astype(int),
+            'distance2': clusterAssment[:, 1],
+            'accessment': clusterAssment,
+            'iterations': iterations
+    }
+    return result
+