scikit-learn · agramfort · Sep 2, 2010
diff --git a/scikits/learn/preprocessing.py b/scikits/learn/preprocessing.py
@@ -0,0 +1,31 @@
+import numpy as np
+
+from .base import BaseEstimator
+
+class Scaler(BaseEstimator):
+    """Object to standardize a dataset along any axis
+
+    It centers the dataset and optionaly scales to 
+    fix the variance to 1.
+
+    """
+    def __init__(self, axis=0, with_std=True):
+        self.axis = axis
+        self.with_std = with_std
+
+    def fit(self, X, y=None, **params):
+        self._set_params(**params)
+        X = np.rollaxis(X, self.axis)
+        self.mean = X.mean(axis=0)
+        if self.with_std:
+            self.std = X.std(axis=0)
+        return self
+
+    def transform(self, X, y=None, copy=True):
+        if copy:
+            X = X.copy()
+        Xr = np.rollaxis(X, self.axis)
+        Xr -= self.mean
+        if self.with_std:
+            Xr /= self.std
+        return X
diff --git a/scikits/learn/tests/test_preprocessing.py b/scikits/learn/tests/test_preprocessing.py
@@ -0,0 +1,20 @@
+import numpy as np
+
+from numpy.testing import assert_array_almost_equal
+
+from scikits.learn.preprocessing import Scaler
+
+def test_scaler():
+    """Test scaling of dataset along all axis
+    """
+
+    X = np.random.randn(4, 5)
+
+    scaler = Scaler(axis=1)
+    X_scaled = scaler.fit(X).transform(X, copy=False)
+    assert_array_almost_equal(X_scaled.mean(axis=1), 4*[0.0])
+    assert_array_almost_equal(X_scaled.std(axis=1), 4*[1.0])
+
+    scaler = Scaler(axis=0, with_std=False)
+    X_scaled = scaler.fit(X).transform(X, copy=False)
+    assert_array_almost_equal(X_scaled.mean(axis=0), 5*[0.0])