Fixed unitary tests

pygtftk/stats/intersect/modl/apriori.py

@@ -24,6 +24,9 @@ def __init__(self, min_support):
 
         To run apriori, use the following steps, if X is a matrix with one line per transaction, one column per item and a '1' if the item is presnet in the transaction :
 
+        >>> X = [[1,1,0],[1,0,1]]
+        >>> names = ['A','B','C']
+        >>> from pygtftk.stats.intersect.modl.apriori import Apriori, matrix_to_list_of_transactions, apriori_results_to_matrix
         >>> transactions = matrix_to_list_of_transactions(X, names)
         >>> myminer = Apriori(min_support = 0)
         >>> myminer.run_apriori(transactions)
@@ -153,7 +156,9 @@ def matrix_to_list_of_transactions(x, names):
     From a matrix with one line per transaction and one column per element with 1 if present and 0 if absent, 
     returns a list of transaction
     """
+    # Enforce type
     names = np.array(names)
+    x = np.array(x)
 
     # Get the list of all nonzero elements in each row
     result = []

pygtftk/stats/intersect/modl/subroutines.py

@@ -240,8 +240,8 @@ def normalize_and_jitter_matrix_rows(X, normalize = True, jitter = True):
     >>> from pygtftk.stats.intersect.modl.subroutines import normalize_and_jitter_matrix_rows
     >>> D = np.array([[1,1,0],[0,1,0],[0,1,1]])
     >>> Dc = normalize_and_jitter_matrix_rows(D)
-    >>> Dc_theory = [[0, 1, 0], [3.534E-4, 7.071E-1, 7.071E-1], [7.071E-1, 7.071E-1, 5E-4]]
-    >>> npt.assert_almost_equal(Dc, Dc_theory, decimal = 4)
+    >>> Dc_theory = [[0, 1, 0], [7.0736E-5, 7.07107E-1, 7.07107E-1], [7.07107E-1, 7.07107E-1, 9.99859E-05]]
+    >>> npt.assert_almost_equal(Dc, Dc_theory, decimal = 5)
     """
 
     X = np.array(X) # Enforce NumPy array
@@ -449,10 +449,4 @@ def manhattan_dist_with_sparsity(X_true, X_rebuilt, code):
     best_words = list(set(dictionary))
     best_words.remove(tuple([0] * nb_features))
 
-    # If the words had been normalized, un-normalize them.
-    if normalize_words:
-        best_words = np.array(best_words).astype(bool).astype(int)
-
-
-
     return best_words