fixed a issue for array ordering

alphamind/aggregate.pyx

@@ -13,7 +13,6 @@ from libc.math cimport sqrt
 from libc.math cimport fabs
 from libc.stdlib cimport malloc
 from libc.stdlib cimport free
-from numpy import array
 from libcpp.vector cimport vector as cpp_vector
 from libcpp.unordered_map cimport unordered_map as cpp_map
 from cython.operator cimport dereference as deref
@@ -220,6 +219,8 @@ cpdef np.ndarray[double, ndim=2] transform(long[:] groups, double[:, :] x, str f
     cdef size_t loop_idx1
     cdef size_t loop_idx2
 
+    x = np.ascontiguousarray(x)
+
     try:
         if func == 'mean':
             value_data_ptr = agg_mean(mapped_groups, max_g[0], &x[0, 0], length, width)
@@ -258,6 +259,8 @@ cpdef np.ndarray[double, ndim=2] aggregate(long[:] groups, double[:, :] x, str f
     cdef double* value_data_ptr
     cdef np.ndarray[double, ndim=2] res
 
+    x = np.ascontiguousarray(x)
+
     try:
         if func == 'mean':
             value_data_ptr = agg_mean(mapped_groups, max_g[0], &x[0, 0], length, width)

alphamind/data/neutralize.py

@@ -50,13 +50,13 @@ def neutralize(x: np.ndarray, y: np.ndarray, groups: np.ndarray=None, output_exp
 
         if output_explained:
             explained = ls_explain(x, b)
-        elif output_exposure:
+        if output_exposure:
             exposure = b
 
     output_dict = {}
     if output_explained:
         output_dict['explained'] = explained
-    elif output_exposure:
+    if output_exposure:
         output_dict['exposure'] = exposure
 
     if output_dict:
@@ -95,5 +95,4 @@ if __name__ == '__main__':
     y = np.random.randn(3000, 2)
     groups = np.random.randint(30, size=3000)
 
-    b = ls_fit(x, y)
-    ls_explained(x, y, b)
\ No newline at end of file
+    print(neutralize(x, y, groups, output_explained=True, output_exposure=True))

alphamind/data/standardize.py

@@ -6,12 +6,14 @@ Created on 2017-4-25
 """
 
 import numpy as np
-
+from numpy import ascontiguousarray
 from alphamind.aggregate import transform
 
 
 def standardize(x: np.ndarray, groups: np.ndarray=None) -> np.ndarray:
 
+    x = ascontiguousarray(x)
+
     if groups is not None:
         mean_values = transform(groups, x, 'mean')
         std_values = transform(groups, x, 'std')
@@ -19,3 +21,13 @@ def standardize(x: np.ndarray, groups: np.ndarray=None) -> np.ndarray:
         return (x - mean_values) / std_values
     else:
         return (x - x.mean(axis=0)) / x.std(axis=0)
+
+
+if __name__ == '__main__':
+    import pandas as pd
+
+    df = pd.read_csv('d:/test_data.csv', index_col=0)
+
+    x = df.values
+    groups = df.index.values.astype(int)
+    standardize(x, groups)

alphamind/data/winsorize.py

@@ -6,11 +6,12 @@ Created on 2017-4-25
 """
 
 import numpy as np
-
+from numpy import ascontiguousarray
 from alphamind.aggregate import transform
 
 
 def winsorize_normal(x: np.ndarray, num_stds: int=3, groups: np.ndarray=None) -> np.ndarray:
+    x = ascontiguousarray(x)
 
     if groups is not None:
         mean_values = transform(groups, x, 'mean')