Merge pull request #1 from jdagilliland/master

Repo cleanup

Author: jesolem

.gitignore

@@ -0,0 +1 @@
+*.pyc

PCV/__init__.pyc

@@ -3,61 +3,61 @@
 
 class BayesClassifier(object):
 
-	def __init__(self):
-		""" Initialize classifier with training data. """
-		
-		self.labels = []	# class labels
-		self.mean = []		# class mean
-		self.var = []		# class variances
-		self.n = 0			# nbr of classes
-		
-	def train(self,data,labels=None):
-		""" Train on data (list of arrays n*dim). 
-			Labels are optional, default is 0...n-1. """
-		
-		if labels==None:
-			labels = range(len(data))
-		self.labels = labels
-		self.n = len(labels)
-				
-		for c in data:
-			self.mean.append(mean(c,axis=0))
-			self.var.append(var(c,axis=0))
-		
-	def classify(self,points):
-		""" Classify the points by computing probabilities 
-			for each class and return most probable label. """
+    def __init__(self):
+        """ Initialize classifier with training data. """
 
-		# compute probabilities for each class
-		est_prob = array([gauss(m,v,points) for m,v in zip(self.mean,self.var)])
-				
-		print 'est prob',est_prob.shape,self.labels
-		# get index of highest probability, this gives class label
-		ndx = est_prob.argmax(axis=0)
-		
-		est_labels = array([self.labels[n] for n in ndx])
-		
-		return est_labels, est_prob
+        self.labels = []    # class labels
+        self.mean = []        # class mean
+        self.var = []        # class variances
+        self.n = 0            # nbr of classes
+        
+    def train(self,data,labels=None):
+        """ Train on data (list of arrays n*dim). 
+            Labels are optional, default is 0...n-1. """
+        
+        if labels==None:
+            labels = range(len(data))
+        self.labels = labels
+        self.n = len(labels)
+                
+        for c in data:
+            self.mean.append(mean(c,axis=0))
+            self.var.append(var(c,axis=0))
+        
+    def classify(self,points):
+        """ Classify the points by computing probabilities 
+            for each class and return most probable label. """
+        
+        # compute probabilities for each class
+        est_prob = array([gauss(m,v,points) for m,v in zip(self.mean,self.var)])
+                
+        print 'est prob',est_prob.shape,self.labels
+        # get index of highest probability, this gives class label
+        ndx = est_prob.argmax(axis=0)
+        
+        est_labels = array([self.labels[n] for n in ndx])
+        
+        return est_labels, est_prob
 
 
 def gauss(m,v,x):
-	""" Evaluate Gaussian in d-dimensions with independent 
-		mean m and variance v at the points in (the rows of) x. 
-		http://en.wikipedia.org/wiki/Multivariate_normal_distribution """
-	
-	if len(x.shape)==1:
-		n,d = 1,x.shape[0]
-	else:
-		n,d = x.shape
-			
-	# covariance matrix, subtract mean
-	S = diag(1/v)
-	x = x-m
-	# product of probabilities
-	y = exp(-0.5*diag(dot(x,dot(S,x.T))))
-	
-	# normalize and return
-	return y * (2*pi)**(-d/2.0) / ( sqrt(prod(v)) + 1e-6)
+    """ Evaluate Gaussian in d-dimensions with independent 
+        mean m and variance v at the points in (the rows of) x. 
+        http://en.wikipedia.org/wiki/Multivariate_normal_distribution """
+    
+    if len(x.shape)==1:
+        n,d = 1,x.shape[0]
+    else:
+        n,d = x.shape
+            
+    # covariance matrix, subtract mean
+    S = diag(1/v)
+    x = x-m
+    # product of probabilities
+    y = exp(-0.5*diag(dot(x,dot(S,x.T))))
+    
+    # normalize and return
+    return y * (2*pi)**(-d/2.0) / ( sqrt(prod(v)) + 1e-6)
 
 
 

PCV/classifiers/__init__.pyc

@@ -1,35 +1,35 @@
 from numpy import * 
 
 class KnnClassifier(object):
-	
-	def __init__(self,labels,samples):
-		""" Initialize classifier with training data. """
-		
-		self.labels = labels
-		self.samples = samples
-	
-	def classify(self,point,k=3):
-		""" Classify a point against k nearest 
-			in the training data, return label. """
-		
-		# compute distance to all training points
-		dist = array([L2dist(point,s) for s in self.samples])
-		
-		# sort them
-		ndx = dist.argsort()
-		
-		# use dictionary to store the k nearest
-		votes = {}
-		for i in range(k):
-			label = self.labels[ndx[i]]
-			votes.setdefault(label,0)
-			votes[label] += 1
-		    
-		return max(votes)
+    
+    def __init__(self,labels,samples):
+        """ Initialize classifier with training data. """
+        
+        self.labels = labels
+        self.samples = samples
+    
+    def classify(self,point,k=3):
+        """ Classify a point against k nearest 
+            in the training data, return label. """
+        
+        # compute distance to all training points
+        dist = array([L2dist(point,s) for s in self.samples])
+        
+        # sort them
+        ndx = dist.argsort()
+        
+        # use dictionary to store the k nearest
+        votes = {}
+        for i in range(k):
+            label = self.labels[ndx[i]]
+            votes.setdefault(label,0)
+            votes[label] += 1
+            
+        return max(votes)
 
 
 def L2dist(p1,p2):
-	return sqrt( sum( (p1-p2)**2) )
+    return sqrt( sum( (p1-p2)**2) )
 
 def L1dist(v1,v2):
-	return sum(abs(v1-v2))
+    return sum(abs(v1-v2))