onetime kmean is done

Mapping/kmean_clustering/kmean_clustering.py

@@ -2,34 +2,83 @@
 
 Object clustering with k-mean algorithm
 
-
 author: Atsushi Sakai (@Atsushi_twi)
 
 """
 
+import numpy as np
+import math
 import matplotlib.pyplot as plt
 import random
 
 
-class Cluster:
+class Clusters:
 
-    def __init__(self):
-        self.x = []
-        self.y = []
-        self.cx = None
-        self.cy = None
+    def __init__(self, x, y, nlabel):
+        self.x = x
+        self.y = y
+        self.ndata = len(self.x)
+        self.nlabel = nlabel
+        self.labels = [random.randint(0, nlabel - 1)
+                       for _ in range(self.ndata)]
+        self.cx = [0.0 for _ in range(nlabel)]
+        self.cy = [0.0 for _ in range(nlabel)]
+
+
+def init_clusters(rx, ry, nc):
+
+    clusters = Clusters(rx, ry, nc)
+
+    return clusters
+
+
+def calc_centroid(clusters):
+
+    for ic in range(clusters.nlabel):
+        x, y = calc_labeled_points(ic, clusters)
+        ndata = len(x)
+        clusters.cx[ic] = sum(x) / ndata
+        clusters.cy[ic] = sum(y) / ndata
+
+    return clusters
+
+
+def update_clusters(clusters):
+    cost = 0.0
+
+    for ip in range(clusters.ndata):
+        px = clusters.x[ip]
+        py = clusters.y[ip]
+
+        dx = [icx - px for icx in clusters.cx]
+        dy = [icy - py for icy in clusters.cy]
+
+        dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
+        mind = min(dlist)
+        min_id = dlist.index(mind)
+        clusters.labels[ip] = min_id
+        cost += min_id
+
+    return clusters, cost
 
 
 def kmean_clustering(rx, ry, nc):
 
-    minx, maxx = min(rx), max(rx)
-    miny, maxy = min(ry), max(ry)
+    clusters = init_clusters(rx, ry, nc)
+    clusters = calc_centroid(clusters)
 
-    clusters = [Cluster() for i in range(nc)]
+    MAX_LOOP = 10
+    DCOST_TH = 1.0
+    pcost = 100.0
+    for loop in range(MAX_LOOP):
+        print("Loop:", loop)
+        clusters, cost = update_clusters(clusters)
+        clusters = calc_centroid(clusters)
 
-    for c in clusters:
-        c.cx = random.uniform(minx, maxx)
-        c.cy = random.uniform(miny, maxy)
+        dcost = abs(cost - pcost)
+        if dcost < DCOST_TH:
+            break
+        pcost = cost
 
     return clusters
 
@@ -40,17 +89,30 @@ def calc_raw_data():
 
     cx = [0.0, 5.0]
     cy = [0.0, 5.0]
-    np = 30
+    npoints = 30
     rand_d = 3.0
 
     for (icx, icy) in zip(cx, cy):
-        for _ in range(np):
+        for _ in range(npoints):
             rx.append(icx + rand_d * (random.random() - 0.5))
             ry.append(icy + rand_d * (random.random() - 0.5))
 
     return rx, ry
 
 
+def calc_labeled_points(ic, clusters):
+
+    inds = np.array([i for i in range(clusters.ndata)
+                     if clusters.labels[i] == ic])
+    tx = np.array(clusters.x)
+    ty = np.array(clusters.y)
+
+    x = tx[inds]
+    y = ty[inds]
+
+    return x, y
+
+
 def main():
     print(__file__ + " start!!")
 
@@ -59,11 +121,10 @@ def main():
     ncluster = 2
     clusters = kmean_clustering(rx, ry, ncluster)
 
-    for c in clusters:
-        print(c.cx, c.cy)
-        plt.plot(c.cx, c.cy, "x")
-
-    plt.plot(rx, ry, ".")
+    for ic in range(clusters.nlabel):
+        x, y = calc_labeled_points(ic, clusters)
+        plt.plot(x, y, "x")
+    plt.plot(clusters.cx, clusters.cy, "o")
     plt.show()