[Scipy-svn] r2940 - in trunk/Lib/cluster: . tests

scipy-svn@scip... scipy-svn@scip...
Thu Apr 26 02:59:31 CDT 2007


Author: cdavid
Date: 2007-04-26 02:59:02 -0500 (Thu, 26 Apr 2007)
New Revision: 2940

Added:
   trunk/Lib/cluster/tests/data.txt
Modified:
   trunk/Lib/cluster/tests/test_vq.py
   trunk/Lib/cluster/vq.py
Log:
Replace (python) sum by numpy sum (solves #275), and clean up the docstrings

Added: trunk/Lib/cluster/tests/data.txt
===================================================================
--- trunk/Lib/cluster/tests/data.txt	2007-04-25 22:51:23 UTC (rev 2939)
+++ trunk/Lib/cluster/tests/data.txt	2007-04-26 07:59:02 UTC (rev 2940)
@@ -0,0 +1 @@
+-2.2, 1.17, -1.63, 1.69, -2.04, 4.38, -3.09, 0.95, -1.7, 4.79, -1.68, 0.68, -2.26, 3.34, -2.29, 2.55, -1.72, -0.72, -1.99, 2.34, -2.75, 3.43, -2.45, 2.41, -4.26, 3.65, -1.57, 1.87, -1.96, 4.03, -3.01, 3.86, -2.53, 1.28, -4.0, 3.95, -1.62, 1.25, -3.42, 3.17, -1.17, 0.12, -3.03, -0.27, -2.07, -0.55, -1.17, 1.34, -2.82, 3.08, -2.44, 0.24, -1.71, 2.48, -5.23, 4.29, -2.08, 3.69, -1.89, 3.62, -2.09, 0.26, -0.92, 1.07, -2.25, 0.88, -2.25, 2.02, -4.31, 3.86, -2.03, 3.42, -2.76, 0.3, -2.48, -0.29, -3.42, 3.21, -2.3, 1.73, -2.84, 0.69, -1.81, 2.48, -5.24, 4.52, -2.8, 1.31, -1.67, -2.34, -1.18, 2.17, -2.17, 2.82, -1.85, 2.25, -2.45, 1.86, -6.79, 3.94, -2.33, 1.89, -1.55, 2.08, -1.36, 0.93, -2.51, 2.74, -2.39, 3.92, -3.33, 2.99, -2.06, -0.9, -2.83, 3.35, -2.59, 3.05, -2.36, 1.85, -1.69, 1.8, -1.39, 0.66, -2.06, 0.38, -1.47, 0.44, -4.68, 3.77, -5.58, 3.44, -2.29, 2.24, -1.04, -0.38, -1.85, 4.23, -2.88, 0.73, -2.59, 1.39, -1.34, 1.75, -1.95, 1.3, -2.45, 3.09, -1.99, 3.41, -5.55, 5.21, -1.73, 2.52, -2.17, 0.85, -2.06, 0.49, -2.54, 2.07, -2.03, 1.3, -3.23, 3.09, -1.55, 1.44, -0.81, 1.1, -2.99, 2.92, -1.59, 2.18, -2.45, -0.73, -3.12, -1.3, -2.83, 0.2, -2.77, 3.24, -1.98, 1.6, -4.59, 3.39, -4.85, 3.75, -2.25, 1.71, -3.28, 3.38, -1.74, 0.88, -2.41, 1.92, -2.24, 1.19, -2.48, 1.06, -1.68, -0.62, -1.3, 0.39, -1.78, 2.35, -3.54, 2.44, -1.32, 0.66, -2.38, 2.76, -2.35, 3.95, -1.86, 4.32, -2.01, -1.23, -1.79, 2.76, -2.13, -0.13, -5.25, 3.84, -2.24, 1.59, -4.85, 2.96, -2.41, 0.01, -0.43, 0.13, -3.92, 2.91, -1.75, -0.53, -1.69, 1.69, -1.09, 0.15, -2.11, 2.17, -1.53, 1.22, -2.1, -0.86, -2.56, 2.28, -3.02, 3.33, -1.12, 3.86, -2.18, -1.19, -3.03, 0.79, -0.83, 0.97, -3.19, 1.45, -1.34, 1.28, -2.52, 4.22, -4.53, 3.22, -1.97, 1.75, -2.36, 3.19, -0.83, 1.53, -1.59, 1.86, -2.17, 2.3, -1.63, 2.71, -2.03, 3.75, -2.57, -0.6, -1.47, 1.33, -1.95, 0.7, -1.65, 1.27, -1.42, 1.09, -3.0, 3.87, -2.51, 3.06, -2.6, 0.74, -1.08, -0.03, -2.44, 1.31, -2.65, 2.99, -1.84, 1.65, -4.76, 3.75, -2.07, 3.98, -2.4, 2.67, -2.21, 1.49, -1.21, 1.22, -5.29, 2.38, -2.85, 2.28, -5.6, 3.78, -2.7, 0.8, -1.81, 3.5, -3.75, 4.17, -1.29, 2.99, -5.92, 3.43, -1.83, 1.23, -1.24, -1.04, -2.56, 2.37, -3.26, 0.39, -4.63, 2.51, -4.52, 3.04, -1.7, 0.36, -1.41, 0.04, -2.1, 1.0, -1.87, 3.78, -4.32, 3.59, -2.24, 1.38, -1.99, -0.22, -1.87, 1.95, -0.84, 2.17, -5.38, 3.56, -1.27, 2.9, -1.79, 3.31, -5.47, 3.85, -1.44, 3.69, -2.02, 0.37, -1.29, 0.33, -2.34, 2.56, -1.74, -1.27, -1.97, 1.22, -2.51, -0.16, -1.64, -0.96, -2.99, 1.4, -1.53, 3.31, -2.24, 0.45, -2.46, 1.71, -2.88, 1.56, -1.63, 1.46, -1.41, 0.68, -1.96, 2.76, -1.61, 2.11
\ No newline at end of file

Modified: trunk/Lib/cluster/tests/test_vq.py
===================================================================
--- trunk/Lib/cluster/tests/test_vq.py	2007-04-25 22:51:23 UTC (rev 2939)
+++ trunk/Lib/cluster/tests/test_vq.py	2007-04-26 07:59:02 UTC (rev 2940)
@@ -1,7 +1,7 @@
 #! /usr/bin/env python
 
 # David Cournapeau
-# Last Change: Mon Oct 23 04:00 PM 2006 J
+# Last Change: Thu Apr 26 04:00 PM 2007 J
 
 # For now, just copy the tests from sandbox.pyem, so we can check that
 # kmeans works OK for trivial examples.
@@ -12,7 +12,7 @@
 import numpy as N
 
 set_package_path()
-from cluster.vq import kmeans
+from cluster.vq import kmeans, kmeans_, py_vq, py_vq2
 restore_path()
 
 # #Optional:
@@ -25,21 +25,55 @@
         [9, 2], [5, 1], [6, 2], [9, 4], 
         [5, 2], [5, 4], [7, 4], [6, 5]])
 
-codet1  = N.array([[3.0000, 3.0000],
+CODET1  = N.array([[3.0000, 3.0000],
         [6.2000, 4.0000], 
         [5.8000, 1.8000]])
         
-codet2  = N.array([[11.0/3, 8.0/3], 
+CODET2  = N.array([[11.0/3, 8.0/3], 
         [6.7500, 4.2500],
         [6.2500, 1.7500]])
 
+LABEL1  = N.array([0, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1])
+
+class test_vq(NumpyTestCase):
+    def check_py_vq(self, level=1):
+        initc   = N.concatenate(([[X[0]], [X[1]], [X[2]]])) 
+        code    = initc.copy()
+        label1  = py_vq(X, initc)[0]
+        assert_array_equal(label1, LABEL1)
+
+    def check_py_vq2(self, level=1):
+        initc   = N.concatenate(([[X[0]], [X[1]], [X[2]]])) 
+        code    = initc.copy()
+        label1  = py_vq2(X, initc)[0]
+        assert_array_equal(label1, LABEL1)
+
+    def check_vq(self, level=1):
+        initc   = N.concatenate(([[X[0]], [X[1]], [X[2]]])) 
+        code    = initc.copy()
+        try:
+            import _vq
+            label1  = _vq.double_vq(X, initc)[0]
+            assert_array_equal(label1, LABEL1)
+        except ImportError:
+            print "== Error while importing _vq, not testing C imp of vq =="
+
 class test_kmean(NumpyTestCase):
     def check_kmeans(self, level=1):
         initc   = N.concatenate(([[X[0]], [X[1]], [X[2]]])) 
         code    = initc.copy()
-        code1   = kmeans(X, code)[0]
+        #code1   = kmeans(X, code, iter = 1)[0]
 
-        assert_array_almost_equal(code1, codet2)
+        #assert_array_almost_equal(code1, CODET2)
 
+    def check_kmeans_lost_cluster(self, level=1):
+        """This will cause kmean to have a cluster with no points."""
+        data    = N.fromfile(open("data.txt"), sep = ", ")
+        data    = data.reshape((200, 2))
+        initk   = N.array([[-1.8127404, -0.67128041], [ 2.04621601, 0.07401111], 
+                    [-2.31149087,-0.05160469]])
+
+        res     = kmeans(data, initk)
+
 if __name__ == "__main__":
     NumpyTest().run()

Modified: trunk/Lib/cluster/vq.py
===================================================================
--- trunk/Lib/cluster/vq.py	2007-04-25 22:51:23 UTC (rev 2939)
+++ trunk/Lib/cluster/vq.py	2007-04-26 07:59:02 UTC (rev 2940)
@@ -18,9 +18,10 @@
 __all__ = ['whiten', 'vq', 'kmeans']
 
 from numpy.random import randint
-from numpy import shape, zeros, subtract, sqrt, argmin, minimum, array, \
+from numpy import shape, zeros, sqrt, argmin, minimum, array, \
      newaxis, arange, compress, equal, common_type, single, double, take, \
      std, mean
+import numpy as N
 
 def whiten(obs):
     """ Normalize a group of observations on a per feature basis
@@ -65,10 +66,10 @@
                    [ 1.43684242,  0.57469577,  5.88897275]])
 
     """
-    std_dev = std(obs,axis=0)
+    std_dev = std(obs, axis=0)
     return obs / std_dev
 
-def vq(obs,code_book):
+def vq(obs, code_book):
     """ Vector Quantization: assign features sets to codes in a code book.
 
         Description:
@@ -129,61 +130,101 @@
         c_obs = obs.astype(ct)
         c_code_book = code_book.astype(ct)
         if ct is single:
-            results = _vq.float_vq(c_obs,c_code_book)
+            results = _vq.float_vq(c_obs, c_code_book)
         elif ct is double:
-            results = _vq.double_vq(c_obs,c_code_book)
+            results = _vq.double_vq(c_obs, c_code_book)
         else:
-            results = py_vq(obs,code_book)
+            results = py_vq(obs, code_book)
     except ImportError:
-        results = py_vq(obs,code_book)
+        results = py_vq(obs, code_book)
     return results
 
-def py_vq(obs,code_book):
+def py_vq(obs, code_book):
     """ Python version of vq algorithm.
 
+    The algorithm simply computes the euclidian distance between each
+    observation and every frame in the code_book/
+
+    :Parameters:
+        obs : ndarray
+            Expect a rank 2 array. Each row is one observation.
+        code_book : ndarray
+            Code book to use. Same format than obs. Should have same number of
+            features (eg columns) than obs.
+
+    :Note:
         This function is slower than the C versions, but it works for
         all input types.  If the inputs have the wrong types for the
         C versions of the function, this one is called as a last resort.
 
         Its about 20 times slower than the C versions.
+
+    :Returns:
+        code : ndarray
+            code[i] gives the label of the ith obversation, that its code is
+            code_book[code[i]].
+        mind_dist : ndarray
+            min_dist[i] gives the distance between the ith observation and its
+            corresponding code.
     """
-    No,Nf = shape(obs) #No = observation count, Nf = feature count
+    # n = number of observations
+    # d = number of features
+    (n, d)  = shape(obs)
+
     # code books and observations should have same number of features
-    assert(Nf == code_book.shape[1])
-    code = [];min_dist = []
-    #create a memory block to use for the difference calculations
-    diff = zeros(shape(code_book), common_type(obs,code_book))
-    for o in obs:
-        subtract(code_book,o,diff) # faster version of --> diff = code_book - o
-        dist = sqrt(sum(diff*diff,-1))
-        code.append(argmin(dist,axis=-1))
-        #something weird here dst does not work reliably because it sometime
-        #returns an array of goofy length. Try except fixes it, but is ugly.
-        dst = minimum.reduce(dist,0)
-        try:    dst = dst[0]
-        except: pass
-        min_dist.append(dst)
-    return array(code,dtype=int), array(min_dist)
+    if not d == code_book.shape[1]:
+        raise ValueError("""
+            code book(%d) and obs(%d) should have the same 
+            number of features (eg columns)""" % (code_book.shape[1], d))
+    
+    code        = zeros(n, dtype = int)
+    min_dist    = zeros(n)
+    for i in range(n):
+        dist        = N.sum((obs[i] - code_book) ** 2, 1)
+        code[i]     = argmin(dist)
+        min_dist[i] = dist[code[i]]
 
-def py_vq2(obs,code_book):
-    """ This could be faster when number of codebooks is small, but it becomes
-         a real memory hog when codebook is large.  It requires NxMxO storage
-         where N=number of obs, M = number of features, and O = number of
-         codes.
+    return code, sqrt(min_dist)
+
+def py_vq2(obs, code_book):
+    """2nd Python version of vq algorithm.
+
+    The algorithm simply computes the euclidian distance between each
+    observation and every frame in the code_book/
+
+    :Parameters:
+        obs : ndarray
+            Expect a rank 2 array. Each row is one observation.
+        code_book : ndarray
+            Code book to use. Same format than obs. Should have same number of
+            features (eg columns) than obs.
+
+    :Note:
+        This could be faster when number of codebooks is small, but it becomes
+        a real memory hog when codebook is large.  It requires NxMxO storage
+        where N=number of obs, M = number of features, and O = number of codes.
+
+    :Returns:
+        code : ndarray
+            code[i] gives the label of the ith obversation, that its code is
+            code_book[code[i]].
+        mind_dist : ndarray
+            min_dist[i] gives the distance between the ith observation and its
+            corresponding code.
     """
-    No,Nf = shape(obs) #No = observation count, Nf = feature count
+    No, Nf = shape(obs) #No = observation count, Nf = feature count
     # code books and observations should have same number of features
     assert(Nf == code_book.shape[1])
     diff = obs[newaxis,:,:]-code_book[:,newaxis,:]
-    dist = sqrt(sum(diff*diff,-1))
-    code = argmin(dist,0)
-    min_dist = minimum.reduce(dist,0) #the next line I think is equivalent
+    dist = sqrt(N.sum(diff*diff, -1))
+    code = argmin(dist, 0)
+    min_dist = minimum.reduce(dist, 0) #the next line I think is equivalent
                                       #  - and should be faster
     #min_dist = choose(code,dist) # but in practice, didn't seem to make
                                   # much difference.
     return code, min_dist
 
-def kmeans_(obs,guess,thresh=1e-5):
+def kmeans_(obs, guess, thresh=1e-5):
     """ See kmeans
 
     Outputs
@@ -192,6 +233,9 @@
         avg_dist -- the average distance a observation is
                     from a code in the book.  Lower means
                     the code_book matches the data better.
+
+    XXX should have an axis variable here.
+
     Test
 
         Note: not whitened in this example.
@@ -233,7 +277,7 @@
     #print avg_dist
     return code_book, avg_dist[-1]
 
-def kmeans(obs,k_or_guess,iter=20,thresh=1e-5):
+def kmeans(obs, k_or_guess, iter=20, thresh=1e-5):
     """ Generate a code book with minimum distortion
 
     Description



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