tonegas/nnodely | Build 13520426074 | tests/test_network_element.py | Coveralls

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import unittest, sys, os, torch

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from nnodely import *

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from nnodely.relation import Stream

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from nnodely import relation

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import numpy as np

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relation.CHECK_NAMES = False

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from nnodely.logger import logging, nnLogger

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log = nnLogger(__name__, logging.CRITICAL)

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log.setAllLevel(logging.CRITICAL)

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sys.path.append(os.getcwd())

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class ModelyNetworkBuildingTest(unittest.TestCase):

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    def TestAlmostEqual(self, data1, data2, precision=4):

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        assert np.asarray(data1, dtype=np.float32).ndim == np.asarray(data2, dtype=np.float32).ndim, f'Inputs must have the same dimension! Received {type(data1)} and {type(data2)}'

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        if type(data1) == type(data2) == list:

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            self.assertEqual(len(data1),len(data2))

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            for pred, label in zip(data1, data2):

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                self.TestAlmostEqual(pred, label, precision=precision)

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            self.assertAlmostEqual(data1, data2, places=precision)

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    def test_network_building_very_simple(self):

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        input1 = Input('in1').last()

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        rel1 = Fir(input1)

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        fun = Output('out', rel1)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[1, 1], [1, 1]]

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        for ind, (key, value) in enumerate({k: v for k, v in test.model.relation_forward.items() if 'Fir' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_building_simple(self):

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        Stream.reset_count()

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        input1 = Input('in1')

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        rel1 = Fir(input1.tw(0.05))

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        rel2 = Fir(input1.tw(0.01))

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        fun = Output('out',rel1+rel2)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = {'Fir2':[5,1],'Fir5':[1,1]}

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        for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():

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            self.assertEqual(list_of_dimensions[key],list(value.weights.shape))

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    def test_network_building_tw(self):

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        Stream.reset_count()

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        input1 = Input('in1')

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        input2 = Input('in2')

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        rel1 = Fir(input1.tw(0.05))

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        rel2 = Fir(input1.tw(0.01))

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        rel3 = Fir(input2.tw(0.05))

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        rel4 = Fir(input2.tw([-0.02,0.02]))

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        fun = Output('out',rel1+rel2+rel3+rel4)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = {'Fir2':[5,1], 'Fir5':[1,1], 'Fir8':[5,1], 'Fir11':[4,1]}

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        for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():

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            self.assertEqual(list_of_dimensions[key],list(value.weights.shape))

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    def test_network_building_tw2(self):

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        Stream.reset_count()

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        input2 = Input('in2')

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        rel3 = Fir(input2.tw(0.05))

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        rel4 = Fir(input2.tw([-0.02,0.02]))

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        rel5 = Fir(input2.tw([-0.03,0.03]))

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        rel6 = Fir(input2.tw([-0.03, 0]))

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        rel7 = Fir(input2.tw(0.03))

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        fun = Output('out',rel3+rel4+rel5+rel6+rel7)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        self.assertEqual(test.max_n_samples, 8) # 5 samples + 3 samples of the horizon

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        self.assertEqual({'in2': 8} , test.input_n_samples)

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        list_of_dimensions = {'Fir2':[5,1], 'Fir5':[4,1], 'Fir8':[6,1], 'Fir11':[3,1], 'Fir14':[3,1]}

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        for  key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():

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            self.assertEqual(list_of_dimensions[key],list(value.weights.shape))

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    def test_network_building_tw3(self):

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        input2 = Input('in2')

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        rel3 = Fir(input2.tw(0.05))

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        rel4 = Fir(input2.tw([-0.01,0.03]))

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        rel5 = Fir(input2.tw([-0.04,0.01]))

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        fun = Output('out',rel3+rel4+rel5)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[5,1], [4,1], [5,1]]

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        for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_building_tw_with_offest(self):

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        Stream.reset_count()

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        input2 = Input('in2')

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        rel3 = Fir(input2.tw(0.05))

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        rel4 = Fir(input2.tw([-0.04,0.02]))

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        rel5 = Fir(input2.tw([-0.04,0.02],offset=0))

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        rel6 = Fir(input2.tw([-0.04,0.02],offset=0.01))

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        fun = Output('out',rel3+rel4+rel5+rel6)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = {'Fir2':[5,1], 'Fir5':[6,1], 'Fir8':[6,1], 'Fir11':[6,1]}

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        for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():

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            self.assertEqual(list_of_dimensions[key],list(value.weights.shape))

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    def test_network_building_tw_negative(self):

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        input2 = Input('in2')

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        rel1 = Fir(input2.tw([-0.04,-0.01]))

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        rel2 = Fir(input2.tw([-0.06,-0.03]))

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        fun = Output('out',rel1+rel2)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[3,1], [3,1]]

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        for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_building_tw_positive(self):

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        input2 = Input('in2')

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        rel1 = Fir(input2.tw([0.01,0.04]))

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        rel2 = Fir(input2.tw([0.03,0.06]))

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        fun = Output('out',rel1+rel2)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[3,1], [3,1]]

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        for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_building_sw_with_offset(self):

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        Stream.reset_count()

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        input2 = Input('in2')

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        rel3 = Fir(input2.sw(5))

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        rel4 = Fir(input2.sw([-4,2]))

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        rel5 = Fir(input2.sw([-4,2],offset=0))

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        rel6 = Fir(input2.sw([-4,2],offset=1))

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        fun = Output('out',rel3+rel4+rel5+rel6)

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        test = Modely(visualizer=None, seed=1)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = {'Fir2':[5,1], 'Fir5':[6,1], 'Fir8':[6,1], 'Fir11':[6,1]}

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        for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():

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            self.assertEqual(list_of_dimensions[key],list(value.weights.shape))

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    def test_network_building_sw_and_tw(self):

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        input2 = Input('in2')

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        with self.assertRaises(ValueError):

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            input2.sw(5)+input2.tw(0.05)

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        rel1 = Fir(input2.sw([-4,2]))+Fir(input2.tw([-0.01,0]))

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        fun = Output('out',rel1)

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        test = Modely(visualizer=None)

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        test.addModel('fun',fun)

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[6,1], [1,1]]

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        for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_linear(self):

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        torch.manual_seed(1)

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        input = Input('in1')

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        rel1 = Linear(input.sw([-4,2]))

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        rel2 = Linear(5)(input.sw([-1, 2]))

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        fun1 = Output('out1',rel1)

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        fun2 = Output('out2', rel2)

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        input5 = Input('in5', dimensions=3)

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        rel15 = Linear(input5.sw([-4,2]))

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        rel25 = Linear(5)(input5.last())

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        fun15 = Output('out51',rel15)

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        fun25 = Output('out52', rel25)

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        test = Modely(visualizer=None)

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        test.addModel('fun',[fun1,fun2,fun15,fun25])

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        test.neuralizeModel(0.01)

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        list_of_dimensions = [[1,1,1],[1,1,5],[1,3,1],[1,3,5]]

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        for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Linear' in k}.items()):

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            self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))

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    def test_network_linear_interpolation_train(self):

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        torch.manual_seed(1)

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        x = Input('x')

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        param = Parameter(name='a', sw=1)

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        rel1 = Fir(parameter=param)(Interpolation(x_points=[1.0, 2.0, 3.0, 4.0],y_points=[2.0, 4.0, 6.0, 8.0], mode='linear')(x.last()))

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        out = Output('out',rel1)

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        test = Modely(visualizer=None)

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        test.addModel('fun',[out])

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        test.addMinimize('error', out, x.last())

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        test.neuralizeModel(0.01)

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        dataset = {'x':np.random.uniform(1,4,100)}

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        test.loadData(name='dataset', source=dataset)

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        test.trainModel(num_of_epochs=100, train_batch_size=10)

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        self.assertAlmostEqual(test.model.all_parameters['a'].item(), 0.5, places=2)

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    def test_network_linear_interpolation(self):

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        torch.manual_seed(1)

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        x = Input('x')

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        rel1 = Interpolation(x_points=[1.0, 2.0, 3.0, 4.0],y_points=[1.0, 4.0, 9.0, 16.0], mode='linear')(x.last())

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        out = Output('out',rel1)

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        test = Modely(visualizer=None)

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        test.addModel('fun',[out])

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        test.neuralizeModel(0.01)

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        inference = test(inputs={'x':[1.5,2.5,3.5]})

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        self.assertEqual(inference['out'],[2.5,6.5,12.5])

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        torch.manual_seed(1)

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        x = Input('x')

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        rel1 = Interpolation(x_points=[1.0, 4.0, 3.0, 2.0],y_points=[1.0, 16.0, 9.0, 4.0], mode='linear')(x.last())

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        out = Output('out',rel1)

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        test = Modely(visualizer=None)

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        test.addModel('fun',[out])

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        test.neuralizeModel(0.01)

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        inference = test(inputs={'x':[1.5,2.5,3.5]})

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        self.assertEqual(inference['out'],[2.5,6.5,12.5])

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    def test_softmax_and_sigmoid(self):

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        torch.manual_seed(1)

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        x = Input('x')

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        y = Input('y', dimensions=3)

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        softmax = Softmax(y.last())

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        sigmoid = Sigmoid(x.last())

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        out = Output('softmax',softmax)

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        out2 = Output('sigmoid',sigmoid)

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        test = Modely(visualizer=None)

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        test.addModel('model',[out,out2])

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        test.neuralizeModel(0.01)

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        inference = test(inputs={'x':[-1000.0, 0.0, 1000.0], 'y':[[-1.0,0.0,1.0],[-1000.0,0.0,1000.0],[1.0,2.0,3.0]]})

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        self.assertEqual(inference['sigmoid'],[0.0, 0.5, 1.0])

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        self.assertEqual(inference['softmax'],[[[0.09003057330846786, 0.2447284758090973, 0.6652409434318542]],

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    def test_sech_cosh_function(self):

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        torch.manual_seed(1)

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        input = Input('in1')

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        sech_rel = Sech(input.last())

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        sech_rel_2 = Sech(input.sw(2))

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        cosh_rel = Cosh(input.last())

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        cosh_rel_2 = Cosh(input.sw(2))

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        input5 = Input('in5', dimensions=5)

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        sech_rel_5 = Sech(input5.last())

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        cosh_rel_5 = Cosh(input5.last())

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        out1 = Output('sech_out_1', sech_rel)

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        out2 = Output('sech_out_2', sech_rel_2)

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        out3 = Output('sech_out_3', sech_rel_5)

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        out4 = Output('cosh_out_1', cosh_rel)

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        out5 = Output('cosh_out_2', cosh_rel_2)

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        out6 = Output('cosh_out_3', cosh_rel_5)

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        test = Modely(visualizer=None)

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        test.addModel('model',[out1,out2,out3,out4,out5,out6])

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        test.neuralizeModel(0.01)

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        result = test(inputs={'in1':[[3.0],[-2.0]], 'in5':[[4.0,1.0,0.0,-6.0,2.0]]})

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        self.TestAlmostEqual([0.2658022344112396], result['sech_out_1'])

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        self.TestAlmostEqual([[0.0993279218673706, 0.2658022344112396]], result['sech_out_2'])

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        self.TestAlmostEqual([[[0.03661899268627167, 0.6480542421340942, 1.0, 0.004957473836839199, 0.2658022344112396]]], result['sech_out_3'])

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        self.TestAlmostEqual([3.762195587158203], result['cosh_out_1'])

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        self.TestAlmostEqual([[10.067662239074707, 3.762195587158203]], result['cosh_out_2'])

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        self.TestAlmostEqual([[[27.3082332611084, 1.5430806875228882, 1.0, 201.71563720703125, 3.762195587158203]]], result['cosh_out_3'])

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    def test_concatenate_time_concatenate(self):

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        torch.manual_seed(1)

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        input = Input('in1')

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        input2 = Input('in2')

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        concatenate_rel = Concatenate(input.last(),input2.last())

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        timeconcatenate_rel = TimeConcatenate(input.last(),input2.last())

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        concatenate_tw_rel = Concatenate(input.tw(3),input2.tw(3))

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        timeconcatenate_tw_rel = TimeConcatenate(input.tw(3),input2.tw(3))

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        input3 = Input('in3', dimensions=5)

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        input4 = Input('in4', dimensions=5)

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        concatenate_rel_5 = Concatenate(input3.last(),input4.last())

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        timeconcatenate_rel_5 = TimeConcatenate(input3.last(),input4.last())

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        concatenate_tw_rel_5 = Concatenate(input3.tw(3),input4.tw(3))

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        timeconcatenate_tw_rel_5 = TimeConcatenate(input3.tw(3),input4.tw(3))

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        out1 = Output('concatenate', concatenate_rel)

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        out2 = Output('time_concatenate', timeconcatenate_rel)

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        out3 = Output('concatenate_tw', concatenate_tw_rel)

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        out4 = Output('time_concatenate_tw', timeconcatenate_tw_rel)

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        out5 = Output('concatenate_5', concatenate_rel_5)

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        out6 = Output('time_concatenate_5', timeconcatenate_rel_5)

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        out7 = Output('concatenate_tw_5', concatenate_tw_rel_5)

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        out8 = Output('time_concatenate_tw_5', timeconcatenate_tw_rel_5)

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        test = Modely(visualizer=None)

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        test.addModel('model',[out1,out2,out3,out4,out5,out6,out7,out8])

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        test.neuralizeModel(1)

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        result = test(inputs={'in1':[[1.0],[2.0],[3.0]], 'in2':[[4.0],[5.0],[6.0]],

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        self.assertEqual((1,1,2), np.array(result['concatenate']).shape)

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        self.assertEqual([[[3.0, 6.0]]], result['concatenate'])

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        self.assertEqual((1,2), np.array(result['time_concatenate']).shape)

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        self.assertEqual([[3.0, 6.0]], result['time_concatenate'])

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        self.assertEqual((1,3,2), np.array(result['concatenate_tw']).shape)

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        self.assertEqual([[[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]]], result['concatenate_tw'])

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        self.assertEqual((1,6), np.array(result['time_concatenate_tw']).shape)

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        self.assertEqual([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]], result['time_concatenate_tw'])

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        self.assertEqual((1,1,10), np.array(result['concatenate_5']).shape)

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        self.assertEqual([[[17.0, 18.0, 19.0, 20.0, 21.0, 32.0, 33.0, 34.0, 35.0, 36.0]]], result['concatenate_5'])

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348

        self.assertEqual((1,2,5), np.array(result['time_concatenate_5']).shape)

1✔

349

        self.assertEqual([[[17.0, 18.0, 19.0, 20.0, 21.0], [32.0, 33.0, 34.0, 35.0, 36.0]]], result['time_concatenate_5'])

1✔

350

        self.assertEqual((1,3,10), np.array(result['concatenate_tw_5']).shape)

1✔

351

        self.assertEqual([[[7.0, 8.0, 9.0, 10.0, 11.0, 22.0, 23.0, 24.0, 25.0, 26.0],

1✔

354

        self.assertEqual((1,6,5), np.array(result['time_concatenate_tw_5']).shape)

1✔

355

        self.assertEqual([[[7.0, 8.0, 9.0, 10.0, 11.0],

1✔

362

    def test_equation_learner(self):

1✔

363

        x = Input('x')

1✔

364

        F = Input('F')

1✔

366

        def myFun(p1,p2,k1,k2):

1✔

367

            return k1*p1+k2*p2

1✔

369

        K1 = Parameter('k1', dimensions =  1, sw = 1,values=[[2.0]])

1✔

370

        K2 = Parameter('k2', dimensions =  1, sw = 1,values=[[3.0]])

1✔

371

        parfun = ParamFun(myFun, parameters=[K1,K2])

1✔

372

        parfun2 = ParamFun(myFun, parameters=[K1,K2])

1✔

373

        parfun3 = ParamFun(myFun, parameters=[K1,K2])

1✔

374

        fuzzi = Fuzzify(centers=[0,1,2,3])

1✔

376

        linear_layer_in = Linear(output_dimension=3, W_init=init_constant, W_init_params={'value':0}, b_init=init_constant, b_init_params={'value':0}, b=False)

1✔

377

        linear_layer_in_dim_2 = Linear(output_dimension=3, W_init=init_constant, W_init_params={'value':0}, b_init=init_constant, b_init_params={'value':0}, b=False)

1✔

378

        linear_layer_out = Linear(output_dimension=1, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)

1✔

380

        equation_learner = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in)(x.last())

1✔

381

        equation_learner_out = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in, linear_out=linear_layer_out)(x.last())

1✔

382

        equation_learner_tw = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in)(x.sw(2))

1✔

383

        equation_learner_multi_tw = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in_dim_2)((x.sw(2),F.sw(2)))

1✔

385

        linear_layer_in_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1})

1✔

386

        linear_layer_in_2_dim_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1})

1✔

388

        equation_learner_2 = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2)(x.last())

1✔

389

        equation_learner_2_tw = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2)(x.sw(2))

1✔

390

        equation_learner_2_multi_tw = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2_dim_2)((x.sw(2),F.sw(2)))

1✔

392

        linear_layer_in_3 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)

1✔

393

        linear_layer_in_3_dim_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)

1✔

395

        equation_learner_3 = EquationLearner(functions=[parfun, Add, fuzzi], linear_in=linear_layer_in_3)(x.last())

1✔

396

        equation_learner_3_tw = EquationLearner(functions=[parfun2, Add, fuzzi], linear_in=linear_layer_in_3)(x.sw(2))

1✔

397

        equation_learner_3_multi_tw = EquationLearner(functions=[parfun3, Add, fuzzi], linear_in=linear_layer_in_3_dim_2)((x.sw(2),F.sw(2)))

1✔

399

        out = Output('el',equation_learner)

1✔

400

        out2 = Output('el_out',equation_learner_out)

1✔

401

        out3 = Output('el_tw',equation_learner_tw)

1✔

402

        out4 = Output('el_multi_tw',equation_learner_multi_tw)

1✔

403

        out5 = Output('el2',equation_learner_2)

1✔

404

        out6 = Output('el2_tw',equation_learner_2_tw)

1✔

405

        out7 = Output('el2_multi_tw',equation_learner_2_multi_tw)

1✔

406

        out8 = Output('el3',equation_learner_3)

1✔

407

        out9 = Output('el3_tw',equation_learner_3_tw)

1✔

408

        out10 = Output('el3_multi_tw',equation_learner_3_multi_tw)

1✔

409

        example = Modely(visualizer=None)

1✔

410

        example.addModel('model',[out,out2,out3,out4,out5,out6,out7,out8,out9,out10])

1✔

411

        example.neuralizeModel()

1✔

413

        result = example({'x':[[1.0],[2.0]], 'F':[[3.0],[4.0]]})

1✔

414

        self.assertEqual(result['el'], [[[0.0, 0.0, 1.0]]])

1✔

415

        self.assertEqual(result['el_out'], [1.0])

1✔

416

        self.assertEqual(result['el_tw'], [[[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]]])

1✔

417

        self.assertEqual(result['el_multi_tw'], [[[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]]])

1✔

418

        self.assertEqual(result['el2'], [[[4.0, 4.0, 2.0]]])

1✔

419

        self.assertEqual(result['el2_tw'], [[[2.0, 1.0, 1.0], [4.0, 4.0, 2.0]]])

1✔

420

        self.assertEqual(result['el2_multi_tw'], [[[8.0, 16.0, 4.0], [12.0, 36.0, 6.0]]])

1✔

421

        self.assertEqual(result['el3'], [[[10.0, 4.0, 0.0, 0.0, 1.0, 0.0]]])

1✔

422

        self.assertEqual(result['el3_tw'], [[[5.0, 2.0, 0.0, 1.0, 0.0, 0.0], [10.0, 4.0, 0.0, 0.0, 1.0, 0.0]]])

1✔

423

        self.assertEqual(result['el3_multi_tw'], [[[20.0, 8.0, 0.0, 0.0, 0.0, 1.0], [30.0, 12.0, 0.0, 0.0, 0.0, 1.0]]])

1✔

426

if __name__ == '__main__':

1✔

tonegas / nnodely / 13520426074

Source File
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1	import unittest, sys, os, torch	1✔
2
3	from nnodely import *	1✔
4	from nnodely.relation import Stream	1✔
5	from nnodely import relation	1✔
6	import numpy as np	1✔
7	relation.CHECK_NAMES = False	1✔
8
9	from nnodely.logger import logging, nnLogger	1✔
10	log = nnLogger(__name__, logging.CRITICAL)	1✔
11	log.setAllLevel(logging.CRITICAL)	1✔
12
13	sys.path.append(os.getcwd())	1✔
14
15	# 11 Tests
16	# This file tests the dimensions and the of the element created in the pytorch environment
17
18	class ModelyNetworkBuildingTest(unittest.TestCase):	1✔
19
20	def TestAlmostEqual(self, data1, data2, precision=4):	1✔
21	assert np.asarray(data1, dtype=np.float32).ndim == np.asarray(data2, dtype=np.float32).ndim, f'Inputs must have the same dimension! Received {type(data1)} and {type(data2)}'	1✔
22	if type(data1) == type(data2) == list:	1✔
23	self.assertEqual(len(data1),len(data2))	1✔
24	for pred, label in zip(data1, data2):	1✔
25	self.TestAlmostEqual(pred, label, precision=precision)	1✔
26	else:
27	self.assertAlmostEqual(data1, data2, places=precision)	1✔
28
29	def test_network_building_very_simple(self):	1✔
30
31	input1 = Input('in1').last()	1✔
32	rel1 = Fir(input1)	1✔
33	fun = Output('out', rel1)	1✔
34
35	test = Modely(visualizer=None)	1✔
36	test.addModel('fun',fun)	1✔
37	test.neuralizeModel(0.01)	1✔
38
39	list_of_dimensions = [[1, 1], [1, 1]]	1✔
40	for ind, (key, value) in enumerate({k: v for k, v in test.model.relation_forward.items() if 'Fir' in k}.items()):	1✔
41	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
42
43	def test_network_building_simple(self):	1✔
44	Stream.reset_count()	1✔
45	input1 = Input('in1')	1✔
46	rel1 = Fir(input1.tw(0.05))	1✔
47	rel2 = Fir(input1.tw(0.01))	1✔
48	fun = Output('out',rel1+rel2)	1✔
49
50	test = Modely(visualizer=None)	1✔
51	test.addModel('fun',fun)	1✔
52	test.neuralizeModel(0.01)	1✔
53
54	list_of_dimensions = {'Fir2':[5,1],'Fir5':[1,1]}	1✔
55	for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():	1✔
56	self.assertEqual(list_of_dimensions[key],list(value.weights.shape))	1✔
57
58	def test_network_building_tw(self):	1✔
59	Stream.reset_count()	1✔
60	input1 = Input('in1')	1✔
61	input2 = Input('in2')	1✔
62	rel1 = Fir(input1.tw(0.05))	1✔
63	rel2 = Fir(input1.tw(0.01))	1✔
64	rel3 = Fir(input2.tw(0.05))	1✔
65	rel4 = Fir(input2.tw([-0.02,0.02]))	1✔
66	fun = Output('out',rel1+rel2+rel3+rel4)	1✔
67
68	test = Modely(visualizer=None)	1✔
69	test.addModel('fun',fun)	1✔
70	test.neuralizeModel(0.01)	1✔
71
72	list_of_dimensions = {'Fir2':[5,1], 'Fir5':[1,1], 'Fir8':[5,1], 'Fir11':[4,1]}	1✔
73	for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():	1✔
74	self.assertEqual(list_of_dimensions[key],list(value.weights.shape))	1✔
75
76	def test_network_building_tw2(self):	1✔
77	Stream.reset_count()	1✔
78	input2 = Input('in2')	1✔
79	rel3 = Fir(input2.tw(0.05))	1✔
80	rel4 = Fir(input2.tw([-0.02,0.02]))	1✔
81	rel5 = Fir(input2.tw([-0.03,0.03]))	1✔
82	rel6 = Fir(input2.tw([-0.03, 0]))	1✔
83	rel7 = Fir(input2.tw(0.03))	1✔
84	fun = Output('out',rel3+rel4+rel5+rel6+rel7)	1✔
85
86	test = Modely(visualizer=None)	1✔
87	test.addModel('fun',fun)	1✔
88	test.neuralizeModel(0.01)	1✔
89
90	self.assertEqual(test.max_n_samples, 8) # 5 samples + 3 samples of the horizon	1✔
91	self.assertEqual({'in2': 8} , test.input_n_samples)	1✔
92
93	list_of_dimensions = {'Fir2':[5,1], 'Fir5':[4,1], 'Fir8':[6,1], 'Fir11':[3,1], 'Fir14':[3,1]}	1✔
94	for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():	1✔
95	self.assertEqual(list_of_dimensions[key],list(value.weights.shape))	1✔
96
97	def test_network_building_tw3(self):	1✔
98	input2 = Input('in2')	1✔
99	rel3 = Fir(input2.tw(0.05))	1✔
100	rel4 = Fir(input2.tw([-0.01,0.03]))	1✔
101	rel5 = Fir(input2.tw([-0.04,0.01]))	1✔
102	fun = Output('out',rel3+rel4+rel5)	1✔
103
104	test = Modely(visualizer=None)	1✔
105	test.addModel('fun',fun)	1✔
106	test.neuralizeModel(0.01)	1✔
107
108	list_of_dimensions = [[5,1], [4,1], [5,1]]	1✔
109	for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):	1✔
110	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
111
112	def test_network_building_tw_with_offest(self):	1✔
113	Stream.reset_count()	1✔
114	input2 = Input('in2')	1✔
115	rel3 = Fir(input2.tw(0.05))	1✔
116	rel4 = Fir(input2.tw([-0.04,0.02]))	1✔
117	rel5 = Fir(input2.tw([-0.04,0.02],offset=0))	1✔
118	rel6 = Fir(input2.tw([-0.04,0.02],offset=0.01))	1✔
119	fun = Output('out',rel3+rel4+rel5+rel6)	1✔
120
121
122	test = Modely(visualizer=None)	1✔
123	test.addModel('fun',fun)	1✔
124	test.neuralizeModel(0.01)	1✔
125
126	list_of_dimensions = {'Fir2':[5,1], 'Fir5':[6,1], 'Fir8':[6,1], 'Fir11':[6,1]}	1✔
127	for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():	1✔
128	self.assertEqual(list_of_dimensions[key],list(value.weights.shape))	1✔
129
130	def test_network_building_tw_negative(self):	1✔
131	input2 = Input('in2')	1✔
132	rel1 = Fir(input2.tw([-0.04,-0.01]))	1✔
133	rel2 = Fir(input2.tw([-0.06,-0.03]))	1✔
134	fun = Output('out',rel1+rel2)	1✔
135
136	test = Modely(visualizer=None)	1✔
137	test.addModel('fun',fun)	1✔
138	test.neuralizeModel(0.01)	1✔
139
140	list_of_dimensions = [[3,1], [3,1]]	1✔
141	for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):	1✔
142	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
143
144	def test_network_building_tw_positive(self):	1✔
145	input2 = Input('in2')	1✔
146	rel1 = Fir(input2.tw([0.01,0.04]))	1✔
147	rel2 = Fir(input2.tw([0.03,0.06]))	1✔
148	fun = Output('out',rel1+rel2)	1✔
149
150	test = Modely(visualizer=None)	1✔
151	test.addModel('fun',fun)	1✔
152	test.neuralizeModel(0.01)	1✔
153
154	list_of_dimensions = [[3,1], [3,1]]	1✔
155	for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):	1✔
156	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
157
158	def test_network_building_sw_with_offset(self):	1✔
159	Stream.reset_count()	1✔
160	input2 = Input('in2')	1✔
161	rel3 = Fir(input2.sw(5))	1✔
162	rel4 = Fir(input2.sw([-4,2]))	1✔
163	rel5 = Fir(input2.sw([-4,2],offset=0))	1✔
164	rel6 = Fir(input2.sw([-4,2],offset=1))	1✔
165	fun = Output('out',rel3+rel4+rel5+rel6)	1✔
166
167	test = Modely(visualizer=None, seed=1)	1✔
168	test.addModel('fun',fun)	1✔
169	test.neuralizeModel(0.01)	1✔
170
171	list_of_dimensions = {'Fir2':[5,1], 'Fir5':[6,1], 'Fir8':[6,1], 'Fir11':[6,1]}	1✔
172	for key, value in {k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items():	1✔
173	self.assertEqual(list_of_dimensions[key],list(value.weights.shape))	1✔
174
175	def test_network_building_sw_and_tw(self):	1✔
176	input2 = Input('in2')	1✔
177	with self.assertRaises(ValueError):	1✔
178	input2.sw(5)+input2.tw(0.05)	1✔
179
180	rel1 = Fir(input2.sw([-4,2]))+Fir(input2.tw([-0.01,0]))	1✔
181	fun = Output('out',rel1)	1✔
182
183	test = Modely(visualizer=None)	1✔
184	test.addModel('fun',fun)	1✔
185	test.neuralizeModel(0.01)	1✔
186
187	list_of_dimensions = [[6,1], [1,1]]	1✔
188	for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Fir' in k}.items()):	1✔
189	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
190
191	def test_network_linear(self):	1✔
192	torch.manual_seed(1)	1✔
193	input = Input('in1')	1✔
194	rel1 = Linear(input.sw([-4,2]))	1✔
195	rel2 = Linear(5)(input.sw([-1, 2]))	1✔
196	fun1 = Output('out1',rel1)	1✔
197	fun2 = Output('out2', rel2)	1✔
198
199	input5 = Input('in5', dimensions=3)	1✔
200	rel15 = Linear(input5.sw([-4,2]))	1✔
201	rel25 = Linear(5)(input5.last())	1✔
202	fun15 = Output('out51',rel15)	1✔
203	fun25 = Output('out52', rel25)	1✔
204
205	test = Modely(visualizer=None)	1✔
206	test.addModel('fun',[fun1,fun2,fun15,fun25])	1✔
207	test.neuralizeModel(0.01)	1✔
208
209	list_of_dimensions = [[1,1,1],[1,1,5],[1,3,1],[1,3,5]]	1✔
210	for ind, (key, value) in enumerate({k:v for k,v in test.model.relation_forward.items() if 'Linear' in k}.items()):	1✔
211	self.assertEqual(list_of_dimensions[ind],list(value.weights.shape))	1✔
212
213	def test_network_linear_interpolation_train(self):	1✔
214	torch.manual_seed(1)	1✔
215	x = Input('x')	1✔
216	param = Parameter(name='a', sw=1)	1✔
217	rel1 = Fir(parameter=param)(Interpolation(x_points=[1.0, 2.0, 3.0, 4.0],y_points=[2.0, 4.0, 6.0, 8.0], mode='linear')(x.last()))	1✔
218	out = Output('out',rel1)	1✔
219
220	test = Modely(visualizer=None)	1✔
221	test.addModel('fun',[out])	1✔
222	test.addMinimize('error', out, x.last())	1✔
223	test.neuralizeModel(0.01)	1✔
224
225	dataset = {'x':np.random.uniform(1,4,100)}	1✔
226	test.loadData(name='dataset', source=dataset)	1✔
227	test.trainModel(num_of_epochs=100, train_batch_size=10)	1✔
228	self.assertAlmostEqual(test.model.all_parameters['a'].item(), 0.5, places=2)	1✔
229
230	def test_network_linear_interpolation(self):	1✔
231	torch.manual_seed(1)	1✔
232	x = Input('x')	1✔
233	rel1 = Interpolation(x_points=[1.0, 2.0, 3.0, 4.0],y_points=[1.0, 4.0, 9.0, 16.0], mode='linear')(x.last())	1✔
234	out = Output('out',rel1)	1✔
235
236	test = Modely(visualizer=None)	1✔
237	test.addModel('fun',[out])	1✔
238	test.neuralizeModel(0.01)	1✔
239
240	inference = test(inputs={'x':[1.5,2.5,3.5]})	1✔
241	self.assertEqual(inference['out'],[2.5,6.5,12.5])	1✔
242
243	torch.manual_seed(1)	1✔
244	x = Input('x')	1✔
245	rel1 = Interpolation(x_points=[1.0, 4.0, 3.0, 2.0],y_points=[1.0, 16.0, 9.0, 4.0], mode='linear')(x.last())	1✔
246	out = Output('out',rel1)	1✔
247
248	test = Modely(visualizer=None)	1✔
249	test.addModel('fun',[out])	1✔
250	test.neuralizeModel(0.01)	1✔
251
252	inference = test(inputs={'x':[1.5,2.5,3.5]})	1✔
253	self.assertEqual(inference['out'],[2.5,6.5,12.5])	1✔
254
255	def test_softmax_and_sigmoid(self):	1✔
256	torch.manual_seed(1)	1✔
257	x = Input('x')	1✔
258	y = Input('y', dimensions=3)	1✔
259	softmax = Softmax(y.last())	1✔
260	sigmoid = Sigmoid(x.last())	1✔
261	out = Output('softmax',softmax)	1✔
262	out2 = Output('sigmoid',sigmoid)	1✔
263
264	test = Modely(visualizer=None)	1✔
265	test.addModel('model',[out,out2])	1✔
266	test.neuralizeModel(0.01)	1✔
267
268	inference = test(inputs={'x':[-1000.0, 0.0, 1000.0], 'y':[[-1.0,0.0,1.0],[-1000.0,0.0,1000.0],[1.0,2.0,3.0]]})	1✔
269	self.assertEqual(inference['sigmoid'],[0.0, 0.5, 1.0])	1✔
270	self.assertEqual(inference['softmax'],[[[0.09003057330846786, 0.2447284758090973, 0.6652409434318542]],	1✔
271	[[0.0, 0.0, 1.0]],
272	[[0.09003057330846786, 0.2447284758090973, 0.6652409434318542]]])
273
274	def test_sech_cosh_function(self):	1✔
275	torch.manual_seed(1)	1✔
276	input = Input('in1')	1✔
277	sech_rel = Sech(input.last())	1✔
278	sech_rel_2 = Sech(input.sw(2))	1✔
279	cosh_rel = Cosh(input.last())	1✔
280	cosh_rel_2 = Cosh(input.sw(2))	1✔
281
282	input5 = Input('in5', dimensions=5)	1✔
283	sech_rel_5 = Sech(input5.last())	1✔
284	cosh_rel_5 = Cosh(input5.last())	1✔
285
286	out1 = Output('sech_out_1', sech_rel)	1✔
287	out2 = Output('sech_out_2', sech_rel_2)	1✔
288	out3 = Output('sech_out_3', sech_rel_5)	1✔
289	out4 = Output('cosh_out_1', cosh_rel)	1✔
290	out5 = Output('cosh_out_2', cosh_rel_2)	1✔
291	out6 = Output('cosh_out_3', cosh_rel_5)	1✔
292
293	test = Modely(visualizer=None)	1✔
294	test.addModel('model',[out1,out2,out3,out4,out5,out6])	1✔
295	test.neuralizeModel(0.01)	1✔
296
297	result = test(inputs={'in1':[[3.0],[-2.0]], 'in5':[[4.0,1.0,0.0,-6.0,2.0]]})	1✔
298	self.TestAlmostEqual([0.2658022344112396], result['sech_out_1'])	1✔
299	self.TestAlmostEqual([[0.0993279218673706, 0.2658022344112396]], result['sech_out_2'])	1✔
300	self.TestAlmostEqual([[[0.03661899268627167, 0.6480542421340942, 1.0, 0.004957473836839199, 0.2658022344112396]]], result['sech_out_3'])	1✔
301	self.TestAlmostEqual([3.762195587158203], result['cosh_out_1'])	1✔
302	self.TestAlmostEqual([[10.067662239074707, 3.762195587158203]], result['cosh_out_2'])	1✔
303	self.TestAlmostEqual([[[27.3082332611084, 1.5430806875228882, 1.0, 201.71563720703125, 3.762195587158203]]], result['cosh_out_3'])	1✔
304
305	def test_concatenate_time_concatenate(self):	1✔
306	torch.manual_seed(1)	1✔
307	input = Input('in1')	1✔
308	input2 = Input('in2')	1✔
309	concatenate_rel = Concatenate(input.last(),input2.last())	1✔
310	timeconcatenate_rel = TimeConcatenate(input.last(),input2.last())	1✔
311	concatenate_tw_rel = Concatenate(input.tw(3),input2.tw(3))	1✔
312	timeconcatenate_tw_rel = TimeConcatenate(input.tw(3),input2.tw(3))	1✔
313
314	input3 = Input('in3', dimensions=5)	1✔
315	input4 = Input('in4', dimensions=5)	1✔
316
317	concatenate_rel_5 = Concatenate(input3.last(),input4.last())	1✔
318	timeconcatenate_rel_5 = TimeConcatenate(input3.last(),input4.last())	1✔
319	concatenate_tw_rel_5 = Concatenate(input3.tw(3),input4.tw(3))	1✔
320	timeconcatenate_tw_rel_5 = TimeConcatenate(input3.tw(3),input4.tw(3))	1✔
321
322	out1 = Output('concatenate', concatenate_rel)	1✔
323	out2 = Output('time_concatenate', timeconcatenate_rel)	1✔
324	out3 = Output('concatenate_tw', concatenate_tw_rel)	1✔
325	out4 = Output('time_concatenate_tw', timeconcatenate_tw_rel)	1✔
326	out5 = Output('concatenate_5', concatenate_rel_5)	1✔
327	out6 = Output('time_concatenate_5', timeconcatenate_rel_5)	1✔
328	out7 = Output('concatenate_tw_5', concatenate_tw_rel_5)	1✔
329	out8 = Output('time_concatenate_tw_5', timeconcatenate_tw_rel_5)	1✔
330
331	test = Modely(visualizer=None)	1✔
332	test.addModel('model',[out1,out2,out3,out4,out5,out6,out7,out8])	1✔
333	test.neuralizeModel(1)	1✔
334
335	result = test(inputs={'in1':[[1.0],[2.0],[3.0]], 'in2':[[4.0],[5.0],[6.0]],	1✔
336	'in3':[[7.0,8.0,9.0,10.0,11.0],[12.0,13.0,14.0,15.0,16.0],[17.0,18.0,19.0,20.0,21.0]],
337	'in4':[[22.0,23.0,24.0,25.0,26.0],[27.0,28.0,29.0,30.0,31.0],[32.0,33.0,34.0,35.0,36.0]]})
338	self.assertEqual((1,1,2), np.array(result['concatenate']).shape)	1✔
339	self.assertEqual([[[3.0, 6.0]]], result['concatenate'])	1✔
340	self.assertEqual((1,2), np.array(result['time_concatenate']).shape)	1✔
341	self.assertEqual([[3.0, 6.0]], result['time_concatenate'])	1✔
342	self.assertEqual((1,3,2), np.array(result['concatenate_tw']).shape)	1✔
343	self.assertEqual([[[1.0, 4.0], [2.0, 5.0], [3.0, 6.0]]], result['concatenate_tw'])	1✔
344	self.assertEqual((1,6), np.array(result['time_concatenate_tw']).shape)	1✔
345	self.assertEqual([[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]], result['time_concatenate_tw'])	1✔
346	self.assertEqual((1,1,10), np.array(result['concatenate_5']).shape)	1✔
347	self.assertEqual([[[17.0, 18.0, 19.0, 20.0, 21.0, 32.0, 33.0, 34.0, 35.0, 36.0]]], result['concatenate_5'])	1✔
348	self.assertEqual((1,2,5), np.array(result['time_concatenate_5']).shape)	1✔
349	self.assertEqual([[[17.0, 18.0, 19.0, 20.0, 21.0], [32.0, 33.0, 34.0, 35.0, 36.0]]], result['time_concatenate_5'])	1✔
350	self.assertEqual((1,3,10), np.array(result['concatenate_tw_5']).shape)	1✔
351	self.assertEqual([[[7.0, 8.0, 9.0, 10.0, 11.0, 22.0, 23.0, 24.0, 25.0, 26.0],	1✔
352	[12.0, 13.0, 14.0, 15.0, 16.0, 27.0, 28.0, 29.0, 30.0, 31.0],
353	[17.0, 18.0, 19.0, 20.0, 21.0, 32.0, 33.0, 34.0, 35.0, 36.0]]], result['concatenate_tw_5'])
354	self.assertEqual((1,6,5), np.array(result['time_concatenate_tw_5']).shape)	1✔
355	self.assertEqual([[[7.0, 8.0, 9.0, 10.0, 11.0],	1✔
356	[12.0, 13.0, 14.0, 15.0, 16.0],
357	[17.0, 18.0, 19.0, 20.0, 21.0],
358	[22.0, 23.0, 24.0, 25.0, 26.0],
359	[27.0, 28.0, 29.0, 30.0, 31.0],
360	[32.0, 33.0, 34.0, 35.0, 36.0]]], result['time_concatenate_tw_5'])
361
362	def test_equation_learner(self):	1✔
363	x = Input('x')	1✔
364	F = Input('F')	1✔
365
366	def myFun(p1,p2,k1,k2):	1✔
367	return k1p1+k2p2	1✔
368
369	K1 = Parameter('k1', dimensions = 1, sw = 1,values=[[2.0]])	1✔
370	K2 = Parameter('k2', dimensions = 1, sw = 1,values=[[3.0]])	1✔
371	parfun = ParamFun(myFun, parameters=[K1,K2])	1✔
372	parfun2 = ParamFun(myFun, parameters=[K1,K2])	1✔
373	parfun3 = ParamFun(myFun, parameters=[K1,K2])	1✔
374	fuzzi = Fuzzify(centers=[0,1,2,3])	1✔
375
376	linear_layer_in = Linear(output_dimension=3, W_init=init_constant, W_init_params={'value':0}, b_init=init_constant, b_init_params={'value':0}, b=False)	1✔
377	linear_layer_in_dim_2 = Linear(output_dimension=3, W_init=init_constant, W_init_params={'value':0}, b_init=init_constant, b_init_params={'value':0}, b=False)	1✔
378	linear_layer_out = Linear(output_dimension=1, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)	1✔
379
380	equation_learner = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in)(x.last())	1✔
381	equation_learner_out = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in, linear_out=linear_layer_out)(x.last())	1✔
382	equation_learner_tw = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in)(x.sw(2))	1✔
383	equation_learner_multi_tw = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer_in_dim_2)((x.sw(2),F.sw(2)))	1✔
384
385	linear_layer_in_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1})	1✔
386	linear_layer_in_2_dim_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1})	1✔
387
388	equation_learner_2 = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2)(x.last())	1✔
389	equation_learner_2_tw = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2)(x.sw(2))	1✔
390	equation_learner_2_multi_tw = EquationLearner(functions=[Add, Mul, Identity], linear_in=linear_layer_in_2_dim_2)((x.sw(2),F.sw(2)))	1✔
391
392	linear_layer_in_3 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)	1✔
393	linear_layer_in_3_dim_2 = Linear(output_dimension=5, W_init=init_constant, W_init_params={'value':1}, b_init=init_constant, b_init_params={'value':0}, b=False)	1✔
394
395	equation_learner_3 = EquationLearner(functions=[parfun, Add, fuzzi], linear_in=linear_layer_in_3)(x.last())	1✔
396	equation_learner_3_tw = EquationLearner(functions=[parfun2, Add, fuzzi], linear_in=linear_layer_in_3)(x.sw(2))	1✔
397	equation_learner_3_multi_tw = EquationLearner(functions=[parfun3, Add, fuzzi], linear_in=linear_layer_in_3_dim_2)((x.sw(2),F.sw(2)))	1✔
398
399	out = Output('el',equation_learner)	1✔
400	out2 = Output('el_out',equation_learner_out)	1✔
401	out3 = Output('el_tw',equation_learner_tw)	1✔
402	out4 = Output('el_multi_tw',equation_learner_multi_tw)	1✔
403	out5 = Output('el2',equation_learner_2)	1✔
404	out6 = Output('el2_tw',equation_learner_2_tw)	1✔
405	out7 = Output('el2_multi_tw',equation_learner_2_multi_tw)	1✔
406	out8 = Output('el3',equation_learner_3)	1✔
407	out9 = Output('el3_tw',equation_learner_3_tw)	1✔
408	out10 = Output('el3_multi_tw',equation_learner_3_multi_tw)	1✔
409	example = Modely(visualizer=None)	1✔
410	example.addModel('model',[out,out2,out3,out4,out5,out6,out7,out8,out9,out10])	1✔
411	example.neuralizeModel()	1✔
412
413	result = example({'x':[[1.0],[2.0]], 'F':[[3.0],[4.0]]})	1✔
414	self.assertEqual(result['el'], [[[0.0, 0.0, 1.0]]])	1✔
415	self.assertEqual(result['el_out'], [1.0])	1✔
416	self.assertEqual(result['el_tw'], [[[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]]])	1✔
417	self.assertEqual(result['el_multi_tw'], [[[0.0, 0.0, 1.0], [0.0, 0.0, 1.0]]])	1✔
418	self.assertEqual(result['el2'], [[[4.0, 4.0, 2.0]]])	1✔
419	self.assertEqual(result['el2_tw'], [[[2.0, 1.0, 1.0], [4.0, 4.0, 2.0]]])	1✔
420	self.assertEqual(result['el2_multi_tw'], [[[8.0, 16.0, 4.0], [12.0, 36.0, 6.0]]])	1✔
421	self.assertEqual(result['el3'], [[[10.0, 4.0, 0.0, 0.0, 1.0, 0.0]]])	1✔
422	self.assertEqual(result['el3_tw'], [[[5.0, 2.0, 0.0, 1.0, 0.0, 0.0], [10.0, 4.0, 0.0, 0.0, 1.0, 0.0]]])	1✔
423	self.assertEqual(result['el3_multi_tw'], [[[20.0, 8.0, 0.0, 0.0, 0.0, 1.0], [30.0, 12.0, 0.0, 0.0, 0.0, 1.0]]])	1✔
424
425
426	if __name__ == '__main__':	1✔
NEW 427	unittest.main()	×

tonegas / nnodely / 13520426074

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