jvesely/linear_combination

psyneulink/components/functions/function.py

@@ -2162,25 +2162,25 @@ def function(self,
             else:
                 # Hadamard scale, scalar offset
                 if isinstance(offset, numbers.Number):
-                    result = np.product([np.sum([variable], axis=0), scale], axis=0)
+                    result = np.product([np.sum([variable], axis=0), scale], axis=0) + offset
                 # Hadamard scale and offset
                 else:
                     hadamard_product = np.product([np.sum([variable], axis=0), scale], axis=0)
                     result = np.sum(np.append([hadamard_product], [offset], axis=0), axis=0)
 
         elif operation is PRODUCT:
-            product = np.product(variable, axis=0)
+            product = np.product([variable], axis=0)
             if isinstance(scale, numbers.Number):
                 # Scalar scale and offset
                 if isinstance(offset, numbers.Number):
                     result = product * scale + offset
                 # Scalar scale and Hadamard offset
                 else:
-                    result = np.sum(np.append([product], [offset], axis=0), axis=0) + offset
+                    result = np.sum(np.append([product * scale], [offset], axis=0), axis=0)
             else:
                 # Hadamard scale, scalar offset
                 if isinstance(offset, numbers.Number):
-                    result = np.product(np.append([product], [scale], axis=0), axis=0) + offset
+                    result = np.product([product, scale], axis=0) + offset
                 # Hadamard scale and offset
                 else:
                     hadamard_product = np.product(np.append([product], [scale], axis=0), axis=0)

tests/functions/test_combination.py

@@ -1,8 +1,13 @@
 import psyneulink as pnl
+import psyneulink.components.functions.function as Function
+import psyneulink.globals.keywords as kw
 import numpy as np
+import pytest
 
 class TestReduce:
 
+    @pytest.mark.function
+    @pytest.mark.combination_function
     def test_single_array(self):
         R_function = pnl.Reduce(operation=pnl.SUM)
         R_mechanism = pnl.ProcessingMechanism(function=pnl.Reduce(operation=pnl.SUM),
@@ -19,6 +24,8 @@ def test_single_array(self):
         assert np.allclose(R_mechanism.execute([1, 2, 3, 4, 5]), [15.0])
         # assert np.allclose(R_mechanism.execute([[1, 2, 3, 4, 5]]), [15.0])
 
+    @pytest.mark.function
+    @pytest.mark.combination_function
     def test_column_vector(self):
         R_function = pnl.Reduce(operation=pnl.SUM)
         R_mechanism = pnl.ProcessingMechanism(function=pnl.Reduce(operation=pnl.SUM),
@@ -32,6 +39,8 @@ def test_column_vector(self):
         assert np.allclose(R_mechanism.execute([[1], [2], [3], [4], [5]]), [1, 2, 3, 4, 5])
         # assert np.allclose(R_mechanism.execute([[1], [2], [3], [4], [5]]), [15.0])
 
+    @pytest.mark.function
+    @pytest.mark.combination_function
     def test_matrix(self):
         R_function = pnl.Reduce(operation=pnl.SUM)
         R_mechanism = pnl.ProcessingMechanism(function=pnl.Reduce(operation=pnl.SUM),
@@ -55,3 +64,51 @@ def test_matrix(self):
     #     # print("mech = ", R_mechanism.execute([[[1, 2], [3, 4, 5], [6, 7, 8, 9]]]))
     #     # print("mech = ", R_mechanism.execute([[[1, 2], [3, 4, 5], [6, 7, 8, 9]]]))
     #
+
+
+SIZE=5
+#This gives us the correct 2d array
+test_var = np.random.rand(1, SIZE)
+
+RAND1_V = np.random.rand(1, SIZE)
+RAND2_V = np.random.rand(1, SIZE)
+RAND3_V = np.random.rand(1, SIZE)
+
+RAND1_S = np.random.rand()
+RAND2_S = np.random.rand()
+RAND3_S = np.random.rand()
+
+test_linear_combination_data = [
+    (Function.LinearCombination, test_var, {'scale':RAND1_S, 'offset':RAND2_S, 'operation':pnl.SUM}, test_var * RAND1_S + RAND2_S),
+    (Function.LinearCombination, test_var, {'scale':RAND1_S, 'offset':RAND2_V, 'operation':pnl.SUM}, test_var * RAND1_S + RAND2_V),
+    (Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_S, 'operation':pnl.SUM}, test_var * RAND1_V + RAND2_S),
+    (Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_V, 'operation':pnl.SUM}, test_var * RAND1_V + RAND2_V),
+
+    (Function.LinearCombination, test_var, {'scale':RAND1_S, 'offset':RAND2_S, 'operation':pnl.PRODUCT}, test_var * RAND1_S + RAND2_S),
+    (Function.LinearCombination, test_var, {'scale':RAND1_S, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, test_var * RAND1_S + RAND2_V),
+    (Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_S, 'operation':pnl.PRODUCT}, test_var * RAND1_V + RAND2_S),
+    (Function.LinearCombination, test_var, {'scale':RAND1_V, 'offset':RAND2_V, 'operation':pnl.PRODUCT}, test_var * RAND1_V + RAND2_V),
+]
+
+# use list, naming function produces ugly names
+linear_combination_names = [
+    "COMBINE-1 SUM",
+    "COMBINE-1 SUM VECTOR OFFSET",
+    "COMBINE-1 SUM VECTOR SCALE",
+    "COMBINE-1 SUM VECTOR OFFSET SCALE",
+
+    "COMBINE-1 PRODUCT",
+    "COMBINE-1 PRODUCT VECTOR OFFSET",
+    "COMBINE-1 PRODUCT VECTOR SCALE",
+    "COMBINE-1 PRODUCT VECTOR OFFSET SCALE",
+]
+
+@pytest.mark.function
+@pytest.mark.combination_function
+@pytest.mark.parametrize("func, variable, params, expected", test_linear_combination_data, ids=linear_combination_names)
+@pytest.mark.benchmark
+def test_linear_combination_function(func, variable, params, expected, benchmark):
+    f = func(default_variable=variable, **params)
+    benchmark.group = "TransferFunction " + func.componentName;
+    res = benchmark(f.function, variable)
+    assert np.allclose(res, expected)