def test_birch_predict():
    # Test the predict method predicts the nearest centroid.
    rng = np.random.RandomState(0)
    X = generate_clustered_data(n_clusters=3, n_features=3,
                                n_samples_per_cluster=10)

    # n_samples * n_samples_per_cluster
    shuffle_indices = np.arange(30)
    rng.shuffle(shuffle_indices)
    X_shuffle = X[shuffle_indices, :]
    brc = Birch(n_clusters=4, threshold=1.)
    brc.fit(X_shuffle)
    centroids = brc.subcluster_centers_
    assert_array_equal(brc.labels_, brc.predict(X_shuffle))
    nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
    assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0) 

def test_birch_predict():
    # Test the predict method predicts the nearest centroid.
    rng = np.random.RandomState(0)
    X = generate_clustered_data(n_clusters=3, n_features=3,
                                n_samples_per_cluster=10)

    # n_samples * n_samples_per_cluster
    shuffle_indices = np.arange(30)
    rng.shuffle(shuffle_indices)
    X_shuffle = X[shuffle_indices, :]
    brc = Birch(n_clusters=4, threshold=1.)
    brc.fit(X_shuffle)
    centroids = brc.subcluster_centers_
    assert_array_equal(brc.labels_, brc.predict(X_shuffle))
    nearest_centroid = pairwise_distances_argmin(X_shuffle, centroids)
    assert_almost_equal(v_measure_score(nearest_centroid, brc.labels_), 1.0) 

def find_clusters(x, n_clusters, current_split):

    current_split_suffled = list(x_split[current_split])[:]
    shuffle(current_split_suffled)
    current_split_suffled = np.array(current_split_suffled)

    centroids = np.array(current_split_suffled[:n_clusters])

    while True:

        # assign labels based on closest centroid
        #print centroids

        #print "len train", len(x_split[current_split])
        labels = pairwise_distances_argmin(x_split[current_split], centroids)
        #print "len labels", len(labels)

        
        # find new centroids as the average of examples
        new_centroids = np.array([x_split[current_split][labels == i].mean(0) for i in range(n_clusters)])
        
        # check for convergence
        if np.all(centroids == new_centroids):
            break
        centroids = new_centroids

    return centroids, labels 

def predict(self, X, set_outliers=True):
        import sklearn.metrics as sk_met
        y = sk_met.pairwise_distances_argmin(X, self.cluster_centers_[:, None])
        if set_outliers:
            y[((X > self.max) | (X < self.min))[:, 0]] = -1
        return y 

def run_pruning_for_conv2d_layer(self, pruning_factor: float, layer: layers.Conv2D, layer_weight_mtx) -> List[int]:
        _, _, _, nb_channels = layer_weight_mtx.shape

        # Initialize KMeans
        nb_of_clusters, _ = self._calculate_number_of_channels_to_keep(pruning_factor, nb_channels)
        kmeans = cluster.KMeans(nb_of_clusters, "k-means++")

        # Fit with the flattened weight matrix
        # (height, width, input_channels, output_channels) -> (output_channels, flattened features)
        layer_weight_mtx_reshaped = layer_weight_mtx.transpose(3, 0, 1, 2).reshape(nb_channels, -1)
        # Apply some fuzz to the weights, to avoid duplicates
        self._apply_fuzz(layer_weight_mtx_reshaped)
        kmeans.fit(layer_weight_mtx_reshaped)

        # If a cluster has only a single member, then that should not be pruned
        # so that point will always be the closest to the cluster center
        closest_point_to_cluster_center_indices = metrics.pairwise_distances_argmin(kmeans.cluster_centers_,
                                                                                    layer_weight_mtx_reshaped)
        # Compute filter indices which can be pruned
        channel_indices = set(np.arange(len(layer_weight_mtx_reshaped)))
        channel_indices_to_keep = set(closest_point_to_cluster_center_indices)
        channel_indices_to_prune = list(channel_indices.difference(channel_indices_to_keep))
        channel_indices_to_keep = list(channel_indices_to_keep)

        if len(channel_indices_to_keep) > nb_of_clusters:
            print("Number of selected channels for pruning is less than expected")
            diff = len(channel_indices_to_keep) - nb_of_clusters
            print("Randomly adding {0} channels for pruning".format(diff))
            np.random.shuffle(channel_indices_to_keep)
            for i in range(diff):
                channel_indices_to_prune.append(channel_indices_to_keep.pop(i))
        elif len(channel_indices_to_keep) < nb_of_clusters:
            print("Number of selected channels for pruning is greater than expected. Leaving too few channels.")
            diff = nb_of_clusters - len(channel_indices_to_keep)
            print("Discarding {0} pruneable channels".format(diff))
            for i in range(diff):
                channel_indices_to_keep.append(channel_indices_to_prune.pop(i))

        if len(channel_indices_to_keep) != nb_of_clusters:
            raise ValueError(
                "Number of clusters {0} is not equal with the selected "
                "pruneable channels {1}".format(nb_of_clusters, len(channel_indices_to_prune)))

        return channel_indices_to_prune