Merge pull request #45 from bsc-wdc/kmeans-fix

Kmeans fix

dislib/cluster/dbscan/base.py

@@ -4,7 +4,7 @@
 from pycompss.api.api import compss_wait_on
 from pycompss.api.task import task
 
-from dislib.cluster.dbscan.classes import Square
+from dislib.cluster.dbscan.classes import Region
 from dislib.data import Dataset
 
 
@@ -101,7 +101,7 @@ def fit(self, dataset):
 
         # Compute dbscan in each region of the grid
         for idx in np.ndindex(grid.shape):
-            grid[idx] = Square(idx, self._eps, grid.shape, region_sizes)
+            grid[idx] = Region(idx, self._eps, grid.shape, region_sizes)
             grid[idx].init_data(sorted_data, grid.shape)
             grid[idx].partial_scan(self._min_samples, self._max_samples)
 

dislib/cluster/dbscan/classes.py

@@ -9,7 +9,7 @@
 _NO_CP = -3
 
 
-class Square(object):
+class Region(object):
     def __init__(self, coord, epsilon, grid_shape, region_sizes):
         self.coord = coord
         self.epsilon = epsilon

dislib/cluster/kmeans/base.py

@@ -62,7 +62,7 @@ def fit(self, dataset):
         -----
         This method modifies the input Dataset by setting the cluster labels.
         """
-        centers = _init_centers(dataset[0], self._n_clusters,
+        centers = _init_centers(dataset.n_features, self._n_clusters,
                                 self._random_state)
         self.centers = compss_wait_on(centers)
 
@@ -105,7 +105,7 @@ def fit_predict(self, dataset):
         for subset in dataset:
             labels.append(_get_label(subset))
 
-        return np.array(compss_wait_on(labels))
+        return np.concatenate(compss_wait_on(labels))
 
     def predict(self, x):
         """ Predict the closest cluster each sample in x belongs to.
@@ -122,9 +122,10 @@ def predict(self, x):
         """
         labels = []
 
-        for x in x:
-            dist = np.linalg.norm(x - self.centers, axis=1)
+        for sample in x:
+            dist = np.linalg.norm(sample - self.centers, axis=1)
             labels.append(np.argmin(dist))
+
         return np.array(labels)
 
     def _converged(self, old_centers, iter):
@@ -154,9 +155,8 @@ def _get_label(subset):
 
 
 @task(returns=np.array)
-def _init_centers(subset, n_clusters, random_state):
+def _init_centers(n_features, n_clusters, random_state):
     np.random.seed(random_state)
-    n_features = subset.samples.shape[1]
     centers = np.random.random((n_clusters, n_features))
     return centers
 

examples/kmeans.py

@@ -67,6 +67,7 @@ def main():
 
     kmeans = KMeans(n_clusters=3, random_state=random_state)
     y_pred = kmeans.fit_predict(dataset)
+
     plt.subplot(224)
     plt.scatter(x_filtered[:, 0], x_filtered[:, 1], c=y_pred)
     centers = kmeans.centers