scikit-learn
diff --git a/‎sklearn/utils/sparsefuncs_fast.pyx
Lines changed: 17 additions & 12 deletions b/‎sklearn/utils/sparsefuncs_fast.pyx
Lines changed: 17 additions & 12 deletions
diff --git a/‎sklearn/utils/tests/test_sparsefuncs.py
Lines changed: 20 additions & 1 deletion b/‎sklearn/utils/tests/test_sparsefuncs.py
Lines changed: 20 additions & 1 deletion
@@ -275,13 +275,15 @@ def incr_mean_variance_axis0(X, last_mean, last_var, unsigned long last_n):
 @cython.wraparound(False)
 @cython.cdivision(True)
 def inplace_csr_row_normalize_l1(X):
-    """Inplace row normalize using the l1 norm"""
-    cdef unsigned int n_samples = X.shape[0]
-    cdef unsigned int n_features = X.shape[1]
+    _inplace_csr_row_normalize_l1(X.data, X.shape, X.indices, X.indptr)
 
-    cdef np.ndarray[DOUBLE, ndim=1] X_data = X.data
-    cdef np.ndarray[int, ndim=1] X_indices = X.indices
-    cdef np.ndarray[int, ndim=1] X_indptr = X.indptr
+
+def _inplace_csr_row_normalize_l1(np.ndarray[floating, ndim=1] X_data, shape,
+                                  np.ndarray[int, ndim=1] X_indices,
+                                  np.ndarray[int, ndim=1] X_indptr):
+    """Inplace row normalize using the l1 norm"""
+    cdef unsigned int n_samples = shape[0]
+    cdef unsigned int n_features = shape[1]
 
     # the column indices for row i are stored in:
     #    indices[indptr[i]:indices[i+1]]
@@ -310,13 +312,16 @@ def inplace_csr_row_normalize_l1(X):
 @cython.wraparound(False)
 @cython.cdivision(True)
 def inplace_csr_row_normalize_l2(X):
-    """Inplace row normalize using the l2 norm"""
-    cdef unsigned int n_samples = X.shape[0]
-    cdef unsigned int n_features = X.shape[1]
+    _inplace_csr_row_normalize_l2(X.data, X.shape, X.indices, X.indptr)
 
-    cdef np.ndarray[DOUBLE, ndim=1] X_data = X.data
-    cdef np.ndarray[int, ndim=1] X_indices = X.indices
-    cdef np.ndarray[int, ndim=1] X_indptr = X.indptr
+
+def _inplace_csr_row_normalize_l2(np.ndarray[floating, ndim=1] X_data,
+                                  shape,
+                                  np.ndarray[int, ndim=1] X_indices,
+                                  np.ndarray[int, ndim=1] X_indptr):
+    """Inplace row normalize using the l2 norm"""
+    cdef unsigned int n_samples = shape[0]
+    cdef unsigned int n_features = shape[1]
 
     cdef unsigned int i
     cdef unsigned int j
 
@@ -5,6 +5,7 @@
 from numpy.testing import (assert_array_almost_equal,
                            assert_array_equal,
                            assert_equal)
+from numpy.random import RandomState
 
 from sklearn.datasets import make_classification
 from sklearn.utils.sparsefuncs import (mean_variance_axis,
@@ -14,7 +15,9 @@
                                        inplace_swap_row, inplace_swap_column,
                                        min_max_axis,
                                        count_nonzero, csc_median_axis_0)
-from sklearn.utils.sparsefuncs_fast import assign_rows_csr
+from sklearn.utils.sparsefuncs_fast import (assign_rows_csr,
+                                            inplace_csr_row_normalize_l1,
+                                            inplace_csr_row_normalize_l2)
 from sklearn.utils.testing import assert_raises
 
 
@@ -479,3 +482,19 @@ def test_csc_row_median():
 
     # Test that it raises an Error for non-csc matrices.
     assert_raises(TypeError, csc_median_axis_0, sp.csr_matrix(X))
+
+
+def test_inplace_normalize():
+    ones = np.ones((10, 1))
+    rs = RandomState(10)
+
+    for inplace_csr_row_normalize in (inplace_csr_row_normalize_l1,
+                                      inplace_csr_row_normalize_l2):
+        for dtype in (np.float64, np.float32):
+            X = rs.randn(10, 5).astype(dtype)
+            X_csr = sp.csr_matrix(X)
+            inplace_csr_row_normalize(X_csr)
+            assert_equal(X_csr.dtype, dtype)
+            if inplace_csr_row_normalize is inplace_csr_row_normalize_l2:
+                X_csr.data **= 2
+            assert_array_almost_equal(np.abs(X_csr).sum(axis=1), ones)