scikit-learn
diff --git a/‎sklearn/utils/sparsefuncs_fast.pyx
Lines changed: 19 additions & 13 deletions b/‎sklearn/utils/sparsefuncs_fast.pyx
Lines changed: 19 additions & 13 deletions
diff --git a/‎sklearn/utils/tests/test_sparsefuncs.py
Lines changed: 29 additions & 1 deletion b/‎sklearn/utils/tests/test_sparsefuncs.py
Lines changed: 29 additions & 1 deletion
@@ -11,6 +11,7 @@ cimport numpy as np
 import numpy as np
 import scipy.sparse as sp
 cimport cython
+from cython cimport floating
 
 np.import_array()
 
@@ -274,13 +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]]
@@ -309,13 +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
@@ -364,7 +370,7 @@ def assign_rows_csr(X,
     """Densify selected rows of a CSR matrix into a preallocated array.
 
     Like out[out_rows] = X[X_rows].toarray() but without copying.
-    Only supported for dtype=np.float64.
+    No-copy supported for both dtype=np.float32 and dtype=np.float64.
 
     Parameters
     ----------
 
@@ -14,7 +14,9 @@
                                        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
 
 
@@ -478,3 +480,29 @@ 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_l1():
+    ones =  np.ones((10, 1))
+
+    X = sp.rand(10, 5, dtype=np.float64, density=.7).tocsr()
+    inplace_csr_row_normalize_l1(X)
+    assert_array_almost_equal(X.sum(axis=1), ones)
+
+    X = sp.rand(10, 5, dtype=np.float32, density=.7).tocsr()
+    inplace_csr_row_normalize_l1(X)
+    assert_array_almost_equal(X.sum(axis=1), ones)
+
+
+def test_inplace_normalize_l2():
+    ones =  np.ones((10, 1))
+
+    X = sp.rand(10, 5, dtype=np.float64, density=.7).tocsr()
+    inplace_csr_row_normalize_l2(X)
+    X.data **= 2
+    assert_array_almost_equal(X.sum(axis=1), ones)
+
+    X = sp.rand(10, 5, dtype=np.float32, density=.7).tocsr()
+    inplace_csr_row_normalize_l2(X)
+    X.data **= 2
+    assert_array_almost_equal(X.sum(axis=1), ones)