add test for kmeans

Mapping/kmean_clustering/kmean_clustering.py

@@ -1,176 +0,0 @@
-"""
-
-Object clustering with k-means algorithm
-
-author: Atsushi Sakai (@Atsushi_twi)
-
-"""
-
-import numpy as np
-import math
-import matplotlib.pyplot as plt
-import random
-
-
-class Clusters:
-
-    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 kmeans_clustering(rx, ry, nc):
-
-    clusters = Clusters(rx, ry, nc)
-    clusters = calc_centroid(clusters)
-
-    MAX_LOOP = 10
-    DCOST_TH = 0.1
-    pcost = 100.0
-    for loop in range(MAX_LOOP):
-        #  print("Loop:", loop)
-        clusters, cost = update_clusters(clusters)
-        clusters = calc_centroid(clusters)
-
-        dcost = abs(cost - pcost)
-        if dcost < DCOST_TH:
-            break
-        pcost = cost
-
-    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 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 calc_raw_data(cx, cy, npoints, rand_d):
-
-    rx, ry = [], []
-
-    for (icx, icy) in zip(cx, cy):
-        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 update_positions(cx, cy):
-
-    DX1 = 0.4
-    DY1 = 0.5
-
-    cx[0] += DX1
-    cy[0] += DY1
-
-    DX2 = -0.3
-    DY2 = -0.5
-
-    cx[1] += DX2
-    cy[1] += DY2
-
-    return cx, cy
-
-
-def calc_association(cx, cy, clusters):
-
-    inds = []
-
-    for ic in range(len(cx)):
-        tcx = cx[ic]
-        tcy = cy[ic]
-
-        dx = [icx - tcx for icx in clusters.cx]
-        dy = [icy - tcy for icy in clusters.cy]
-
-        dlist = [math.sqrt(idx**2 + idy**2) for (idx, idy) in zip(dx, dy)]
-        min_id = dlist.index(min(dlist))
-        inds.append(min_id)
-
-    return inds
-
-
-def main():
-    print(__file__ + " start!!")
-
-    cx = [0.0, 8.0]
-    cy = [0.0, 8.0]
-    npoints = 10
-    rand_d = 3.0
-    ncluster = 2
-    sim_time = 15.0
-    dt = 1.0
-    time = 0.0
-
-    while time <= sim_time:
-        print("Time:", time)
-        time += dt
-
-        # simulate objects
-        cx, cy = update_positions(cx, cy)
-        rx, ry = calc_raw_data(cx, cy, npoints, rand_d)
-
-        clusters = kmeans_clustering(rx, ry, ncluster)
-
-        # for animation
-        plt.cla()
-        inds = calc_association(cx, cy, clusters)
-        for ic in inds:
-            x, y = calc_labeled_points(ic, clusters)
-            plt.plot(x, y, "x")
-        plt.plot(cx, cy, "o")
-        plt.xlim(-2.0, 10.0)
-        plt.ylim(-2.0, 10.0)
-        plt.pause(dt)
-
-    print("Done")
-
-
-if __name__ == '__main__':
-    main()

tests/test_kmeans_clustering.py

@@ -0,0 +1,12 @@
+from unittest import TestCase
+
+from Mapping.kmeans_clustering import kmeans_clustering as m
+
+print(__file__)
+
+
+class Test(TestCase):
+
+    def test1(self):
+        m.show_animation = False
+        m.main()