Changing data providers to skeleton code for first lab.

mlp/data_providers.py

@@ -133,10 +133,10 @@ class MNISTDataProvider(DataProvider):
         super(MNISTDataProvider, self).__init__(
             inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
 
-    def next(self):
+    #def next(self):
-        """Returns next data batch or raises `StopIteration` if at end."""
+    #    """Returns next data batch or raises `StopIteration` if at end."""
-        inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
+    #    inputs_batch, targets_batch = super(MNISTDataProvider, self).next()
-        return inputs_batch, self.to_one_of_k(targets_batch)
+    #    return inputs_batch, self.to_one_of_k(targets_batch)
 
     def to_one_of_k(self, int_targets):
         """Converts integer coded class target to 1 of K coded targets.
@@ -153,9 +153,7 @@ class MNISTDataProvider(DataProvider):
             to zero except for the column corresponding to the correct class
             which is equal to one.
         """
-        one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+        raise NotImplementedError()
-        one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
-        return one_of_k_targets
 
 
 class MetOfficeDataProvider(DataProvider):
@@ -179,28 +177,25 @@ class MetOfficeDataProvider(DataProvider):
                 the data before each epoch.
             rng (RandomState): A seeded random number generator.
         """
+        self.window_size = window_size
+        assert window_size > 1, 'window_size must be at least 2.'
         data_path = os.path.join(
             os.environ['MLP_DATA_DIR'], 'HadSSP_daily_qc.txt')
         assert os.path.isfile(data_path), (
             'Data file does not exist at expected path: ' + data_path
         )
-        raw = np.loadtxt(data_path, skiprows=3, usecols=range(2, 32))
+        # load raw data from text file
-        assert window_size > 1, 'window_size must be at least 2.'
+        # ...
-        self.window_size = window_size
         # filter out all missing datapoints and flatten to a vector
-        filtered = raw[raw >= 0].flatten()
+        # ...
         # normalise data to zero mean, unit standard deviation
-        mean = np.mean(filtered)
+        # ...
-        std = np.std(filtered)
+        # convert from flat sequence to windowed data
-        normalised = (filtered - mean) / std
+        # ...
-        # create a view on to array corresponding to a rolling window
-        shape = (normalised.shape[-1] - self.window_size + 1, self.window_size)
-        strides = normalised.strides + (normalised.strides[-1],)
-        windowed = np.lib.stride_tricks.as_strided(
-            normalised, shape=shape, strides=strides)
         # inputs are first (window_size - 1) entries in windows
-        inputs = windowed[:, :-1]
+        # inputs = ...
         # targets are last entry in windows
-        targets = windowed[:, -1]
+        # targets = ...
-        super(MetOfficeDataProvider, self).__init__(
+        # initialise base class with inputs and targets arrays
-            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
+        # super(MetOfficeDataProvider, self).__init__(
+        #     inputs, targets, batch_size, max_num_batches, shuffle_order, rng)