ipf running, ipf stops for max iter and threshold

src/representations.py

@@ -4,6 +4,10 @@
 import Data
 # from sets import Set,ImmutableSet
 
+MAX_IPF_ITER = 100
+IPF_CONV_THRESH = 0.001
+
+
 def calculate_entropy_of_ndarray(probability_matrix):
     calc_cell_entropies = np.vectorize(lambda x: -x*np.log2(x) if x > 0 else 0.)
     return np.sum(calc_cell_entropies(probability_matrix))
@@ -123,14 +127,42 @@ def __init__(self, component_list, dataset):
         # print projection_list
 
         # initialize q:
-        q = np.zeros(self.var_cards)
+        q = np.zeros(var_cards)
         q[:] = 1./q.size # initialize with equal probs
-        # while error > threshold
-        for p in projection_list:
-            # get q's projection, q_proj
-            # update elements of q as below
-            # new_q = old_q * (p/q_proj)
-        # TODO: IPF goes here.
+        assert len(component_list) == len(projection_list)
+        froe2_norm = np.sum(q**2)
+        cont = True
+        itr = 1
+        while (cont and itr < MAX_IPF_ITER):
+            for i,k in enumerate(component_list):
+                var_names = [var.name for var in k.var_list]
+                q_proj = project_q(dataset.variable_names,var_names,q)
+                q = q * (projection_list[i]/q_proj)
+            new_froe2_norm = np.sum(q**2)
+            cont = abs(new_froe2_norm - froe2_norm) > IPF_CONV_THRESH
+            froe2_norm = new_froe2_norm
+            itr += 1
+
+
+def project_q(all_variable_names,variable_list,q):
+    if all(isinstance(variable,int) for variable in variable_list):
+        pointer_list = variable_list
+    elif all(isinstance(variable,str) for variable in variable_list):
+        # TODO convert to int
+        pointer_list = [all_variable_names.index(var)
+                        for var in variable_list]
+    else:
+        raise Exception("invalid variable_list parmeter in \
+                         extract_component.")
+
+    unwanted_variables = [v for v in range(len(all_variable_names)) 
+                          if not v in pointer_list]
+
+    # Keep dims so math works out more easily later and to track
+    # which variables are aggregated.
+    return np.sum(a=q,
+                  axis=tuple(unwanted_variables),
+                  keepdims=True)
 
 
 if __name__ == "__main__":