13531440710

Committed 25 Feb 2025 09:36PM UTC coverage: 95.399% (-0.01%) from 95.411%

Build # 13531440710

Build Type

Pull #65

github

Committed by

web-flow

Commit Message

Merge 1bb29904b into 8d0789317

Pull Request Pull Request #65: 49 general bug fix

Run Details

46 of 53 new or added lines in 8 files covered. (86.79%)

1 existing line in 1 file now uncovered.

10574 of 11084 relevant lines covered (95.4%)

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23.08

/nnodely/visualizer/mplnotebookvisualizer.py

import matplotlib.pyplot as plt

from nnodely.visualizer.textvisualizer import TextVisualizer
from nnodely.fuzzify import return_fuzzify
from nnodely.parametricfunction import return_standard_inputs, return_function
from nnodely.utils import check
from mplplots import plots

class MPLNotebookVisualizer(TextVisualizer):
    def __init__(self, verbose = 1):
        super().__init__(verbose)

    def showEndTraining(self, epoch, train_losses, val_losses):
        for key in self.n4m.model_def['Minimizers'].keys():
            fig = plt.figure()
            ax = fig.add_subplot(111)
            plots.plot_training(ax, "Training", key, train_losses[key], val_losses[key])
        plt.show()

    def showResult(self, name_data):
        super().showResult(name_data)
        for key in self.n4m.model_def['Minimizers'].keys():
            fig = plt.figure()
            ax = fig.add_subplot(111)
            plots.plot_results(ax, name_data, key, self.n4m.prediction[name_data][key]['A'],
                               self.n4m.prediction[name_data][key]['B'], self.n4m.model_def['Info']["SampleTime"])
        plt.show()

    def showWeights(self, weights = None):
        pass

    def showFunctions(self, functions = None, xlim = None, num_points = 1000):
        check(self.n4m.neuralized, ValueError, "The model has not been neuralized.")
        for fun, value in self.n4m.model_def['Functions'].items():
            if fun in functions:
                if 'functions' in self.n4m.model_def['Functions'][fun]:
                    x, activ_fun = return_fuzzify(value, xlim, num_points)
                    fig = plt.figure()
                    ax = fig.add_subplot(111)
                    plots.plot_fuzzy(ax, fun, x, activ_fun, value['centers'])
                    plt.show()
                elif 'code':
                    function_inputs = return_standard_inputs(value, self.n4m.model_def, xlim, num_points)
                    function_output, function_input_list = return_function(value, function_inputs)
                    if value['n_input'] == 2:
                        x0 = function_inputs[0].reshape(num_points, num_points).tolist()
                        x1 = function_inputs[1].reshape(num_points, num_points).tolist()
                        output = function_output.reshape(num_points, num_points).tolist()
                        params = []
                        for i, key in enumerate(value['params_and_consts']):
                            params += [function_inputs[i + value['n_input']].tolist()]
                        plots.plot_3d_function(plt, fun, x0, x1, params, output, function_input_list)
                    else:
                        x = function_inputs[0].reshape(num_points).tolist()
                        output = function_output.reshape(num_points).tolist()
                        params = []
                        for i, key in enumerate(value['params_and_consts']):
                            params += [function_inputs[i + value['n_input']].tolist()]
                        plots.plot_2d_function(plt, fun, x, params, output, function_input_list)
                    plt.show()







1	import matplotlib.pyplot as plt	1✔
2
3	from nnodely.visualizer.textvisualizer import TextVisualizer	1✔
4	from nnodely.fuzzify import return_fuzzify	1✔
5	from nnodely.parametricfunction import return_standard_inputs, return_function	1✔
6	from nnodely.utils import check	1✔
7	from mplplots import plots	1✔
8
9	class MPLNotebookVisualizer(TextVisualizer):	1✔
10	def __init__(self, verbose = 1):	1✔
11	super().__init__(verbose)	×
12
13	def showEndTraining(self, epoch, train_losses, val_losses):	1✔
14	for key in self.n4m.model_def['Minimizers'].keys():	×
15	fig = plt.figure()	×
16	ax = fig.add_subplot(111)	×
17	plots.plot_training(ax, "Training", key, train_losses[key], val_losses[key])	×
18	plt.show()	×
19
20	def showResult(self, name_data):	1✔
21	super().showResult(name_data)	×
22	for key in self.n4m.model_def['Minimizers'].keys():	×
23	fig = plt.figure()	×
24	ax = fig.add_subplot(111)	×
25	plots.plot_results(ax, name_data, key, self.n4m.prediction[name_data][key]['A'],	×
26	self.n4m.prediction[name_data][key]['B'], self.n4m.model_def['Info']["SampleTime"])
27	plt.show()	×
28
29	def showWeights(self, weights = None):	1✔
30	pass	×
31
32	def showFunctions(self, functions = None, xlim = None, num_points = 1000):	1✔
33	check(self.n4m.neuralized, ValueError, "The model has not been neuralized.")	×
34	for fun, value in self.n4m.model_def['Functions'].items():	×
35	if fun in functions:	×
36	if 'functions' in self.n4m.model_def['Functions'][fun]:	×
37	x, activ_fun = return_fuzzify(value, xlim, num_points)	×
38	fig = plt.figure()	×
39	ax = fig.add_subplot(111)	×
40	plots.plot_fuzzy(ax, fun, x, activ_fun, value['centers'])	×
41	plt.show()	×
42	elif 'code':	×
NEW 43	function_inputs = return_standard_inputs(value, self.n4m.model_def, xlim, num_points)	×
44	function_output, function_input_list = return_function(value, function_inputs)	×
45	if value['n_input'] == 2:	×
46	x0 = function_inputs[0].reshape(num_points, num_points).tolist()	×
47	x1 = function_inputs[1].reshape(num_points, num_points).tolist()	×
48	output = function_output.reshape(num_points, num_points).tolist()	×
49	params = []	×
50	for i, key in enumerate(value['params_and_consts']):	×
51	params += [function_inputs[i + value['n_input']].tolist()]	×
52	plots.plot_3d_function(plt, fun, x0, x1, params, output, function_input_list)	×
53	else:
54	x = function_inputs[0].reshape(num_points).tolist()	×
55	output = function_output.reshape(num_points).tolist()	×
56	params = []	×
57	for i, key in enumerate(value['params_and_consts']):	×
58	params += [function_inputs[i + value['n_input']].tolist()]	×
59	plots.plot_2d_function(plt, fun, x, params, output, function_input_list)	×
60	plt.show()	×
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tonegas / nnodely / 13531440710

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