visualization of points

fuzzycmeans/fuzzy_clustering.py

@@ -234,14 +234,16 @@ def fit(self, X, y=None, hard=True):
         """
         y = np.array(y)
         X = np.array(X)
-        if y is not None:
+        if y:
             if hard:
                 self.set_membership_from_hard_cluster(X, y)
         if self.cluster_centers_ is None:
             do_compute_cluster_centers = True
         else:
             do_compute_cluster_centers = False
-
+        if self.u is None:
+            num_of_points = X.shape[0]
+            self.init_membership_random(num_of_points)
         #self.init_membership(X.shape[0])
         list_of_centers = []
         membership_history = []

fuzzycmeans/visualization.py

@@ -6,9 +6,12 @@
 from fuzzy_clustering import FCM
 import logging
 
+
 def color_gen():
     for c in itertools.cycle(pallette[10]):
         yield c
+
+
 colors = color_gen()
 
 
@@ -27,20 +30,20 @@ def mtext(p, x, y, text):
 
     N = 10
 
-    mscatter(p, random(N)+2, random(N)+1, "circle")
-    mscatter(p, random(N)+4, random(N)+1, "square")
-    mscatter(p, random(N)+6, random(N)+1, "triangle")
-    mscatter(p, random(N)+8, random(N)+1, "asterisk")
+    mscatter(p, random(N) + 2, random(N) + 1, "circle")
+    mscatter(p, random(N) + 4, random(N) + 1, "square")
+    mscatter(p, random(N) + 6, random(N) + 1, "triangle")
+    mscatter(p, random(N) + 8, random(N) + 1, "asterisk")
 
-    mscatter(p, random(N)+2, random(N)+4, "circle_x")
-    mscatter(p, random(N)+4, random(N)+4, "square_x")
-    mscatter(p, random(N)+6, random(N)+4, "inverted_triangle")
-    mscatter(p, random(N)+8, random(N)+4, "x")
+    mscatter(p, random(N) + 2, random(N) + 4, "circle_x")
+    mscatter(p, random(N) + 4, random(N) + 4, "square_x")
+    mscatter(p, random(N) + 6, random(N) + 4, "inverted_triangle")
+    mscatter(p, random(N) + 8, random(N) + 4, "x")
 
-    mscatter(p, random(N)+2, random(N)+7, "circle_cross")
-    mscatter(p, random(N)+4, random(N)+7, "square_cross")
-    mscatter(p, random(N)+6, random(N)+7, "diamond")
-    mscatter(p, random(N)+8, random(N)+7, "cross")
+    mscatter(p, random(N) + 2, random(N) + 7, "circle_cross")
+    mscatter(p, random(N) + 4, random(N) + 7, "square_cross")
+    mscatter(p, random(N) + 6, random(N) + 7, "diamond")
+    mscatter(p, random(N) + 8, random(N) + 7, "cross")
 
     mtext(p, 2.5, 0.5, "circle / o")
     mtext(p, 4.5, 0.5, "square")
@@ -62,17 +65,24 @@ def mtext(p, x, y, text):
     show(p)  # open a browser
 
 
-def draw_model_2d(model, show=True):
+def draw_model_2d(model, data=None, membership=None, show_figure=True):
     title = "draw FCM model"
     fig = figure(title=title, toolbar_location=None)
     fig.grid.grid_line_color = None
     fig.background_fill_color = "#eeeeee"
     output_p = None
-    for cc, color in zip(model.cluster_centers_, color_gen()):
-        print cc
-        fig = draw_points_2d(np.array([cc]), fig=fig, title=title, marker="circle", size=15,
-                  line_color="navy", fill_color="orange", alpha=0.5)
-    if show:
+    for clus, cc_color in enumerate(zip(model.cluster_centers_, color_gen())):
+        cc, color = cc_color
+        fig = draw_points_2d(np.array([cc]), fig=fig, title=title, marker="diamond", size=15,
+                             line_color="navy", fill_color=color, alpha=1.0)
+        if data is not None and membership is not None:
+            for idx, data_point in enumerate(data):
+                # print idx
+                # print clus
+                print membership[idx][clus]
+                fig = draw_points_2d(np.array([data_point]), fig=fig, title=title, marker="circle", size=10,
+                                     line_color="navy", fill_color=color, alpha=membership[idx][clus])
+    if show_figure:
         show(fig)
     return fig
 
@@ -84,20 +94,20 @@ def draw_points_2d(points, fig=None, title="figure 123", **kwargs):
         fig.background_fill_color = "#eeeeee"
     x, y = points.T
     fig.scatter(x, y, **kwargs)
-    output_file("output.html", title=title+" of outputfile")
+    output_file("output.html", title=title + " of outputfile")
     return fig
 
 
 # X = np.array([[1, 1], [1, 2], [2, 2], [9, 10], [10, 10], [10, 9], [9, 9]])
 # fcm = FCM()
 # fcm.set_logger(tostdout=True, level=logging.DEBUG)
-# fcm.fit(X, [0, 0, 0, 1, 1, 1, 1])
-# testing_data = np.array([[0, 1.9], [3, 3], [4, 4], [8, 9], [9.5, 6.5]])
+# # fcm.fit(X, [0, 0, 0, 1, 1, 1, 1])
+# fcm.fit(X)
+# testing_data = np.array([[0, 1.9], [3, 3], [4, 4], [8, 9], [9.5, 6.5], [5, 5], [4.5, 4.5], [4.2, 4.2]])
 # predicted_membership = fcm.predict(testing_data)
 # print "\n\ntesting data"
 # print testing_data
 # print "predicted membership"
 # print predicted_membership
 # print "\n\n"
-# draw_model_2d(fcm)
-
+# draw_model_2d(fcm, data=testing_data, membership=predicted_membership)