14319828903

Committed 07 Apr 2025 09:27PM UTC coverage: 97.259% (+0.2%) from 97.035%

Build # 14319828903

Build Type

Pull #86

github

Committed by

web-flow

Commit Message

Merge 44b7c25ee into e9c323c4f

Pull Request Pull Request #86: Smallclasses

Run Details

2275 of 2409 new or added lines in 54 files covered. (94.44%)

1 existing line in 1 file now uncovered.

11637 of 11965 relevant lines covered (97.26%)

0.97 hits per line

Source File
Press 'n' to go to next uncovered line, 'b' for previous

64.81

/nnodely/visualizer/mplnotebookvisualizer.py

import matplotlib.pyplot as plt

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

class MPLNotebookVisualizer(TextVisualizer):
    def __init__(self, verbose = 1, *, test = False):
        super().__init__(verbose)
        if test:
            plt.ion()

    def showEndTraining(self, epoch, train_losses, val_losses):
        for key in self.modely.json['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.modely.json['Minimizers'].keys():
            fig = plt.figure()
            ax = fig.add_subplot(111)
            plots.plot_results(ax, name_data, key, self.modely._prediction[name_data][key]['A'],
                               self.modely._prediction[name_data][key]['B'], self.modely._model_def['Info']["SampleTime"])
        plt.show()

    def showWeights(self, weights = None):
        pass

    def showFunctions(self, functions = None, xlim = None, num_points = 1000):
        check(self.modely.neuralized, ValueError, "The model has not been neuralized.")
        for fun, value in self.modely._model_def['Functions'].items():
            if fun in functions:
                if 'functions' in self.modely._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.modely._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.layers.fuzzify import return_fuzzify	1✔
5	from nnodely.layers.parametricfunction import return_standard_inputs, return_function	1✔
6	from nnodely.support.utils import check	1✔
7	from mplplots import plots	1✔
8
9	class MPLNotebookVisualizer(TextVisualizer):	1✔
10	def __init__(self, verbose = 1, *, test = False):	1✔
11	super().__init__(verbose)	1✔
12	if test:	1✔
13	plt.ion()	1✔
14
15	def showEndTraining(self, epoch, train_losses, val_losses):	1✔
16	for key in self.modely.json['Minimizers'].keys():	1✔
17	fig = plt.figure()	1✔
18	ax = fig.add_subplot(111)	1✔
19	plots.plot_training(ax, "Training", key, train_losses[key], val_losses[key])	1✔
20	plt.show()	1✔
21
22	def showResult(self, name_data):	1✔
23	super().showResult(name_data)	1✔
24	for key in self.modely.json['Minimizers'].keys():	1✔
25	fig = plt.figure()	1✔
26	ax = fig.add_subplot(111)	1✔
27	plots.plot_results(ax, name_data, key, self.modely._prediction[name_data][key]['A'],	1✔
28	self.modely._prediction[name_data][key]['B'], self.modely._model_def['Info']["SampleTime"])
29	plt.show()	1✔
30
31	def showWeights(self, weights = None):	1✔
32	pass	×
33
34	def showFunctions(self, functions = None, xlim = None, num_points = 1000):	1✔
35	check(self.modely.neuralized, ValueError, "The model has not been neuralized.")	1✔
36	for fun, value in self.modely._model_def['Functions'].items():	1✔
37	if fun in functions:	1✔
38	if 'functions' in self.modely._model_def['Functions'][fun]:	1✔
39	x, activ_fun = return_fuzzify(value, xlim, num_points)	1✔
40	fig = plt.figure()	1✔
41	ax = fig.add_subplot(111)	1✔
42	plots.plot_fuzzy(ax, fun, x, activ_fun, value['centers'])	1✔
43	plt.show()	1✔
44	elif 'code':	×
NEW 45	function_inputs = return_standard_inputs(value, self.modely._model_def, xlim, num_points)	×
46	function_output, function_input_list = return_function(value, function_inputs)	×
47	if value['n_input'] == 2:	×
48	x0 = function_inputs[0].reshape(num_points, num_points).tolist()	×
49	x1 = function_inputs[1].reshape(num_points, num_points).tolist()	×
50	output = function_output.reshape(num_points, num_points).tolist()	×
51	params = []	×
52	for i, key in enumerate(value['params_and_consts']):	×
53	params += [function_inputs[i + value['n_input']].tolist()]	×
54	plots.plot_3d_function(plt, fun, x0, x1, params, output, function_input_list)	×
55	else:
56	x = function_inputs[0].reshape(num_points).tolist()	×
57	output = function_output.reshape(num_points).tolist()	×
58	params = []	×
59	for i, key in enumerate(value['params_and_consts']):	×
60	params += [function_inputs[i + value['n_input']].tolist()]	×
61	plots.plot_2d_function(plt, fun, x, params, output, function_input_list)	×
62	plt.show()	×
63
64
65
66
67
68

tonegas / nnodely / 14319828903

Source File Press 'n' to go to next uncovered line, 'b' for previous

Source File
Press 'n' to go to next uncovered line, 'b' for previous