small enhancement

alphamind/data/impl.pyx

@@ -5,8 +5,9 @@ Created on 2017-4-26
 @author: cheng.li
 """
 
-import numpy as np
 cimport numpy as np
+from numpy import zeros
+from numpy import asarray
 cimport cython
 from libc.math cimport sqrt
 
@@ -14,7 +15,7 @@ from libc.math cimport sqrt
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.initializedcheck(False)
-cdef int max_groups(long[:] groups, size_t length) nogil:
+cdef int max_groups(long* groups, size_t length) nogil:
     cdef long curr_max = 0
     cdef size_t i
     cdef long curr
@@ -29,10 +30,12 @@ cdef int max_groups(long[:] groups, size_t length) nogil:
 @cython.wraparound(False)
 @cython.cdivision(True)
 @cython.initializedcheck(False)
-cdef double[:, :] agg_mean(long[:] groups, double[:, :] x, size_t length, size_t width):
+cdef double[:, :] agg_mean(long* groups, double* x, size_t length, size_t width):
     cdef long max_g = max_groups(groups, length)
-    cdef double[:, :] res = np.zeros((max_g+1, width))
-    cdef long[:] bin_count = np.zeros(max_g+1, dtype=int)
+    cdef double[:, :] res = zeros((max_g+1, width))
+    cdef double* res_ptr = &res[0, 0]
+    cdef long[:] bin_count = zeros(max_g+1, dtype=int)
+    cdef long* bin_count_ptr = &bin_count[0]
     cdef size_t i
     cdef size_t j
     cdef long curr
@@ -40,14 +43,14 @@ cdef double[:, :] agg_mean(long[:] groups, double[:, :] x, size_t length, size_t
     with nogil:
         for i in range(length):
             for j in range(width):
-                res[groups[i], j] += x[i, j]
-            bin_count[groups[i]] += 1
+                res_ptr[groups[i]*width + j] += x[i*width + j]
+            bin_count_ptr[groups[i]] += 1
 
-        for i in range(res.shape[0]):
-            curr = bin_count[i]
+        for i in range(max_g+1):
+            curr = bin_count_ptr[i]
             if curr != 0:
                 for j in range(width):
-                    res[i, j] /= curr
+                    res_ptr[i*width + j] /= curr
     return res
 
 
@@ -55,14 +58,18 @@ cdef double[:, :] agg_mean(long[:] groups, double[:, :] x, size_t length, size_t
 @cython.wraparound(False)
 @cython.cdivision(True)
 @cython.initializedcheck(False)
-cdef double[:, :] agg_std(long[:] groups, double[:, :] x, size_t length, size_t width, long ddof=1):
+cdef double[:, :] agg_std(long* groups, double* x, size_t length, size_t width, long ddof=1):
     cdef long max_g = max_groups(groups, length)
-    cdef double[:, :] running_sum_square = np.zeros((max_g+1, width))
-    cdef double[:, :] running_sum = np.zeros((max_g+1, width))
-    cdef long[:] bin_count = np.zeros(max_g+1, dtype=int)
+    cdef double[:, :] running_sum_square = zeros((max_g+1, width))
+    cdef double* running_sum_square_ptr = &running_sum_square[0, 0]
+    cdef double[:, :] running_sum = zeros((max_g+1, width))
+    cdef double* running_sum_ptr = &running_sum[0, 0]
+    cdef long[:] bin_count = zeros(max_g+1, dtype=int)
+    cdef long* bin_count_ptr = &bin_count[0]
     cdef size_t i
     cdef size_t j
     cdef long k
+    cdef size_t indice
     cdef long curr
     cdef double raw_value
 
@@ -70,16 +77,17 @@ cdef double[:, :] agg_std(long[:] groups, double[:, :] x, size_t length, size_t
         for i in range(length):
             k = groups[i]
             for j in range(width):
-                raw_value = x[i, j]
-                running_sum[k, j] += raw_value
-                running_sum_square[k, j] += raw_value * raw_value
-            bin_count[k] += 1
-
-        for i in range(running_sum_square.shape[0]):
-            curr = bin_count[i]
-            if curr > ddof:
+                raw_value = x[i*width + j]
+                running_sum_ptr[k*width + j] += raw_value
+                running_sum_square_ptr[k*width + j] += raw_value * raw_value
+            bin_count_ptr[k] += 1
+
+        for i in range(max_g+1):
+            curr = bin_count_ptr[i]
+            if curr != 0:
                 for j in range(width):
-                    running_sum_square[i, j] = sqrt((running_sum_square[i, j] - running_sum[i, j] * running_sum[i, j] / curr) / (curr - ddof))
+                    indice = i * width + j
+                    running_sum_square_ptr[indice] = sqrt((running_sum_square_ptr[indice] - running_sum_ptr[indice] * running_sum_ptr[indice] / curr) / (curr - ddof))
     return running_sum_square
 
 
@@ -90,21 +98,25 @@ cpdef np.ndarray[double, ndim=2] transform(long[:] groups, double[:, :] x, str f
 
     cdef size_t length = x.shape[0]
     cdef size_t width = x.shape[1]
-    cdef double[:, :] res_data = np.zeros((length, width))
-    cdef double[:, :] value_data = np.zeros((length, width))
+    cdef double[:, :] res_data = zeros((length, width))
+    cdef double* res_data_ptr = &res_data[0, 0]
+    cdef double[:, :] value_data = zeros((length, width))
+    cdef double* value_data_ptr
     cdef size_t i
     cdef size_t j
     cdef size_t k
 
     if func == 'mean':
-        value_data = agg_mean(groups, x, length, width)
+        value_data = agg_mean(&groups[0], &x[0, 0], length, width)
     elif func == 'std':
-        value_data = agg_std(groups, x, length, width, ddof=1)
+        value_data = agg_std(&groups[0], &x[0, 0], length, width, ddof=1)
+
+    value_data_ptr = &value_data[0, 0]
 
     with nogil:
         for i in range(length):
             k = groups[i]
             for j in range(width):
-                res_data[i, j] = value_data[k, j]
+                res_data_ptr[i*width + j] = value_data_ptr[k*width + j]
 
-    return np.asarray(res_data)
\ No newline at end of file
+    return asarray(res_data)
\ No newline at end of file

alphamind/data/standardize.py

@@ -18,4 +18,3 @@ 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)
-