From c82ba5c63a6b5ba1fb2bfb100a22e43a65345979 Mon Sep 17 00:00:00 2001
From: hellboy <j.moudrik@gmail.com>
Date: Mon, 15 Mar 2010 12:13:11 +0100
Subject: [PATCH] kohonen.py: minor change

---
 kohonen_map.py | 36 +++++++++++++++++++++---------------
 1 file changed, 21 insertions(+), 15 deletions(-)

diff --git a/kohonen_map.py b/kohonen_map.py
index b71edaa..2f52c3b 100755
--- a/kohonen_map.py
+++ b/kohonen_map.py
@@ -21,13 +21,14 @@ class ComponentEuclideanMetric(DistanceMetric):
 	def __call__(self, x, y):
 		d = x - y
 		if self.k:
-			return numpy.sqrt((d * d)[k])
+			print k
+			return numpy.sqrt((d * d)[self.k])
 		else:
 			return numpy.sqrt(numpy.sum(d * d, axis=-1))
 
 if __name__ == '__main__':
 	main_pat_filename = Data.main_pat_filename
-	filename = 'koh_all.data'
+	filename = 'koh_all.data2'
 	num_features = 300
 	size=16
 	players_ignore = [ 'Honinbo Shusaku',  'Kuwahara Shusaku', 'Yasuda Shusaku', 'Go Seigen', 'Cho Tae-hyeon','Rui Naiwei']
@@ -56,12 +57,7 @@ if __name__ == '__main__':
 		# Perform a PCA on input vectors
 		input_vectors = pca.process_list_of_vectors(input_vectors)
 	dim = 	len(input_vectors[0])
-
-	m = kohonen.Map(kohonen.Parameters(dimension=dim,
-										shape=(size,size),
-										learning_rate=kohonen.ExponentialTimeseries(-5e-4, 0.5, 0.2),
-										noise_variance=None))
-	m.reset()
+	#print "dim:",dim
 
 	lc =[]
 	num_linc=2000
@@ -79,7 +75,16 @@ if __name__ == '__main__':
 	input_vectors = [numpy.array(vec) for vec in input_vectors]
 	input_vectors_lc = [numpy.array(vec) for vec in lc]
 	total = input_vectors + input_vectors_lc
-	print len(total)
+	#print len(total)
+
+	m = kohonen.Map(kohonen.Parameters(dimension=dim,
+										shape=(size,size),
+										learning_rate=kohonen.ExponentialTimeseries(-5e-4, 0.5, 0.2),
+										noise_variance=None))
+	def def_neurs(*args):
+		return random.choice(total)
+	
+	m.reset(def_neurs)
 
 	print >>sys.stderr, "Training Kohonen net."
 	num_iter = 2000
@@ -87,7 +92,7 @@ if __name__ == '__main__':
 		if i % (num_iter/10) == 0:
 			err = sum( [ m.distances(random.choice(total)).min() for _ in xrange(10) ] ) / 10.0
 			print >>sys.stderr, "%2d%% (%4d): error = %5f alpha = %5f"%(100 * i/num_iter,i, err, m._learning_rate.last)
-			if err < 0.2:
+			if err < 0.08:
 				print >>sys.stderr, "Current error is good enough."
 				break
 			if i > 0 and err > 1.5 and False: 
@@ -98,13 +103,14 @@ if __name__ == '__main__':
 	
 	#im = m.neuron_heatmap()
 	#im.show()
-	'''
+		
 	orig_m = m._metric
-	m._metric = ComponentEuclideanMetric(0)
-	im2 = m.distance_heatmap(input_vectors[0])
-	im2.show()
+	for i in xrange(0):
+		m._metric = ComponentEuclideanMetric(i)
+		im2 = m.neuron_heatmap()
+		im2.show()
 	m._metric = orig_m
-	'''
+	
 
 	winner_neurons= [ m.winner(input_vector) for input_vector in input_vectors ]
 	#print winner_neurons[0]/16, winner_neurons[0]%16
-- 
2.11.4.GIT