refactor naive function

Author: NimaSarajpoor

tests/naive.py

@@ -2379,3 +2379,11 @@ def mpdist_custom_func(P_ABBA, m, percentage, n_A, n_B):
         MPdist = P_ABBA[k]
 
     return MPdist
+
+
+def rolling_window_dot_product(Q, T):
+    window = len(Q)
+    result = np.zeros(len(T) - window + 1)
+    for i in range(len(result)):
+        result[i] = np.dot(T[i : i + window], Q)
+    return result

tests/test_core.py

@@ -35,14 +35,6 @@ def _gpu_searchsorted_kernel(a, v, bfs, nlevel, is_left, idx):
 TEST_THREADS_PER_BLOCK = 10
 
 
-def naive_rolling_window_dot_product(Q, T):
-    window = len(Q)
-    result = np.zeros(len(T) - window + 1)
-    for i in range(len(result)):
-        result[i] = np.dot(T[i : i + window], Q)
-    return result
-
-
 def naive_compute_mean_std_multidimensional(T, m):
     n = T.shape[1]
     nrows, ncols = T.shape
@@ -210,7 +202,7 @@ def test_check_window_size_excl_zone():
 
 @pytest.mark.parametrize("Q, T", test_data)
 def test_sliding_dot_product(Q, T):
-    ref_mp = naive_rolling_window_dot_product(Q, T)
+    ref_mp = naive.rolling_window_dot_product(Q, T)
     comp_mp = core.sliding_dot_product(Q, T)
     npt.assert_almost_equal(ref_mp, comp_mp)
 

tests/test_sdp.py

@@ -1,18 +1,10 @@
+import naive
 import numpy as np
 import pytest
 from numpy import testing as npt
 
 from stumpy import sdp
 
-
-def naive_rolling_window_dot_product(Q, T):
-    window = len(Q)
-    result = np.zeros(len(T) - window + 1)
-    for i in range(len(result)):
-        result[i] = np.dot(T[i : i + window], Q)
-    return result
-
-
 test_data = [
     (np.array([-1, 1, 2], dtype=np.float64), np.array(range(5), dtype=np.float64)),
     (
@@ -25,20 +17,20 @@ def naive_rolling_window_dot_product(Q, T):
 
 @pytest.mark.parametrize("Q, T", test_data)
 def test_njit_sliding_dot_product(Q, T):
-    ref_mp = naive_rolling_window_dot_product(Q, T)
+    ref_mp = naive.rolling_window_dot_product(Q, T)
     comp_mp = sdp._njit_sliding_dot_product(Q, T)
     npt.assert_almost_equal(ref_mp, comp_mp)
 
 
 @pytest.mark.parametrize("Q, T", test_data)
 def test_convolve_sliding_dot_product(Q, T):
-    ref_mp = naive_rolling_window_dot_product(Q, T)
+    ref_mp = naive.rolling_window_dot_product(Q, T)
     comp_mp = sdp._convolve_sliding_dot_product(Q, T)
     npt.assert_almost_equal(ref_mp, comp_mp)
 
 
 @pytest.mark.parametrize("Q, T", test_data)
 def test_sliding_dot_product(Q, T):
-    ref_mp = naive_rolling_window_dot_product(Q, T)
+    ref_mp = naive.rolling_window_dot_product(Q, T)
     comp_mp = sdp._sliding_dot_product(Q, T)
     npt.assert_almost_equal(ref_mp, comp_mp)