Resolve "Adopt to the Estimators API"

Closes #40 (closed)

bob/learn/tensorflow/dataset/__init__.py

 import tensorflow as tf
-
+import numpy
 
 DEFAULT_FEATURE = {'train/data': tf.FixedLenFeature([], tf.string),
                    'train/label': tf.FixedLenFeature([], tf.int64)}
@@ -68,4 +68,80 @@ def append_image_augmentation(image, gray_scale=False,
         image = tf.image.per_image_standardization(image)
 
     return image
+    
+    
+def siamease_pairs_generator(input_data, input_labels):
+    """
+    Giving a list of samples and a list of labels, it dumps a series of
+    pairs for siamese nets.
+    
+    **Parameters**
+
+      input_data: List of whatever representing the data samples
+      
+      input_labels: List of the labels (needs to be in EXACT same order as input_data)
+    """
+
+    # Lists that will be returned
+    left_data = []
+    right_data = []
+    labels = []
+    
+    def append(left, right, label):
+        """
+        Just appending one element in each list
+        """
+        left_data.append(left)
+        right_data.append(right)
+        labels.append(label)
+
+    possible_labels = list(set(input_labels))
+    input_data = numpy.array(input_data)
+    input_labels = numpy.array(input_labels)    
+    total_samples = input_data.shape[0] 
+
+    # Filtering the samples by label and shuffling all the indexes
+    indexes_per_labels = dict()
+    for l in possible_labels:
+        indexes_per_labels[l] = numpy.where(input_labels == l)[0]
+        numpy.random.shuffle(indexes_per_labels[l])
+
+    left_possible_indexes = numpy.random.choice(possible_labels, total_samples, replace=True)
+    right_possible_indexes = numpy.random.choice(possible_labels, total_samples, replace=True)       
+
+    genuine = True
+    for i in range(total_samples):
+
+        if genuine:
+            # Selecting the class
+            class_index = left_possible_indexes[i]
+
+            # Now selecting the samples for the pair
+            left = input_data[indexes_per_labels[class_index][numpy.random.randint(len(indexes_per_labels[class_index]))]]
+            right = input_data[indexes_per_labels[class_index][numpy.random.randint(len(indexes_per_labels[class_index]))]]
+            append(left, right, 0)
+            #yield left, right, 0
+        else:
+            # Selecting the 2 classes
+            class_index = list()
+            class_index.append(left_possible_indexes[i])
+
+            # Finding the right pair
+            j = i
+            # TODO: Lame solution. Fix this
+            while j < total_samples: # Here is an unidiretinal search for the negative pair
+                if left_possible_indexes[i] != right_possible_indexes[j]:
+                    class_index.append(right_possible_indexes[j])
+                    break
+                j += 1
+
+            if j < total_samples:
+                # Now selecting the samples for the pair
+                left = input_data[indexes_per_labels[class_index[0]][numpy.random.randint(len(indexes_per_labels[class_index[0]]))]]
+                right = input_data[indexes_per_labels[class_index[1]][numpy.random.randint(len(indexes_per_labels[class_index[1]]))]]
+                append(left, right, 1)
+
+
+        genuine = not genuine    
+    return left_data, right_data, labels