cnn_visualizer.show_output_dynamic() - Code Metrics - Inspection of "[pyclustering.nnet.cnn] Correction for CNN (not wo..." - annoviko/pyclustering - Measure and Improve Code Quality continuously with Scrutinizer

Completed

Push — master ( e69bed...5c451b )

by Andrei

created 2016-06-21 15:53 UTC

cnn_visualizer.show_output_dynamic() A

↳ Parent: cnn_visualizer

Complexity

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Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
dl	0
loc	3
rs	10
c	0
b	0
f	0

"""!

@brief Chaotic Neural Network
@details Based on article description:
         - 

@authors Andrei Novikov ([email protected])
@date 2014-2016
@copyright GNU Public License

@cond GNU_PUBLIC_LICENSE
    PyClustering is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    
    PyClustering is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.
    
    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <http://www.gnu.org/licenses/>.
@endcond

"""

import matplotlib.pyplot as plt;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import matplotlib.animation as animation;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

import math;
import random;
import numpy;
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

from enum import IntEnum;

from pyclustering.utils import euclidean_distance_sqrt, average_neighbor_distance, heaviside, draw_dynamics;


class type_conn(IntEnum):
    """!
    @brief Enumeration of connection types for Chaotic Neural Network.
    
    @see cnn_network
    
    """
    
    ## All oscillators have connection with each other.
    ALL_TO_ALL  = 0,
    
    ## Connections between oscillators are created in line with Delaunay triangulation.
    TRIANGULATION_DELAUNAY = 1,


class cnn_dynamic:
    def __init__(self, output = [], time = []):
# Bad:
# If array_param is modified inside the function, the next invocation will
# receive the modified object.
def some_function(array_param=[]):
    # ...

# Better: Create an array on each invocation
def some_function(array_param=None):
    array_param = array_param or []
    # ...
        self.output = output;
        self.time = time;


    def __len__(self):
        return len(self.output);


    def allocate_observation_matrix(self):
        number_neurons = len(self.output[0]);
        observation_matrix = [];
        
        for iteration in range(len(self.output)):
            obervation_column = [];
            for index_neuron in range(number_neurons):
                obervation_column.append(heaviside(self.output[iteration][index_neuron]));
            
            observation_matrix.append(obervation_column);
        
        return observation_matrix;


class cnn_visualizer:
    @staticmethod
    def show_output_dynamic(cnn_output_dynamic):
        draw_dynamics(cnn_output_dynamic.time, cnn_output_dynamic.output, x_title = "t", y_title = "x");
    
    
    @staticmethod
    def show_dynamic_matrix(cnn_output_dynamic):
        plt.imshow(cnn_output_dynamic.output, cmap = plt.get_cmap('gray'), interpolation='None', vmin = 0.0, vmax = 1.0); 
        plt.show();
    
    
    @staticmethod
    def show_observation_matrix(cnn_output_dynamic):
        observation_matrix = cnn_output_dynamic.allocate_observation_matrix();
        plt.imshow(observation_matrix, cmap = plt.get_cmap('gray'), interpolation='None', vmin = 0.0, vmax = 1.0); 
        plt.show();


class cnn_network:
    def __init__(self, num_osc, conn_type = type_conn.ALL_TO_ALL, amount_neighbors = 3):
        self.__num_osc = num_osc;
        self.__conn_type = conn_type;
        self.__amount_neighbors = amount_neighbors;
        
        self.__average_distance = 0.0;
        self.__weights = None;
        self.__weights_summary = None;
        
        self.__output = [ random.random() for _ in range(num_osc) ];
    
    
    def __len__(self):
        return self.__num_osc;
    
    
    def simulate(self, steps, stimulus):
        self.__create_weights(stimulus);
        
        dynamic = cnn_dynamic([], []);
        dynamic.output.append(self.__output);
        dynamic.time.append(0);
        
        for step in range(1, steps, 1):
            self.__output = self.__calculate_states();
            
            dynamic.output.append(self.__output);
            dynamic.time.append(step);
            
        return dynamic;
    
    
    def __calculate_states(self):
        output = [ 0.0 for _ in range(self.__num_osc) ];
        
        for i in range(self.__num_osc):
            output[i] = self.__neuron_evolution(i);
        
        return output;
    
    
    def __neuron_evolution(self, index):
        value = 0.0;
        #state = 1.0 - 2.0 * (self.__output[index] ** 2);
        
        for index_neighbor in range(self.__num_osc):
            value += self.__weights[index][index_neighbor] * (1.0 - 2.0 * (self.__output[index_neighbor] ** 2));
        
        return value / self.__weights_summary[index];
    
    
    def __create_weights(self, stimulus):
        self.__average_distance = average_neighbor_distance(stimulus, self.__amount_neighbors);
        print("Average distance: ", self.__average_distance);
        
        self.__weights = [ [ 0.0 for _ in range(len(stimulus)) ] for _ in range(len(stimulus)) ];
        self.__weights_summary = [ 0.0 for _ in range(self.__num_osc) ];
        
        if (self.__conn_type == type_conn.ALL_TO_ALL):
            self.__create_weights_all_to_all(stimulus);
        
        elif (self.__conn_type == type_conn.TRIANGULATION_DELAUNAY):
            self.__create_weights_delaunay_triangulation(stimulus);
    
    
    def __create_weights_all_to_all(self, stimulus):
        for i in range(len(stimulus)):
            for j in range(i + 1, len(stimulus)):
                weight = self.__calculate_weight(stimulus[i], stimulus[j]);
                print(i, j, weight, stimulus[i], stimulus[j]);
                
                self.__weights[i][j] = weight;
                self.__weights[j][i] = weight;
                
                self.__weights_summary[i] += weight;
                self.__weights_summary[j] += weight;
    
    
    def __create_weights_delaunay_triangulation(self, stimulus):
        pass;
    
    
    def __calculate_weight(self, oscillator_location1, oscillator_location2):
        distance = euclidean_distance_sqrt(oscillator_location1, oscillator_location2);
        return math.exp(-distance / (2.0 * self.__average_distance));


1			"""!
2
3			@brief Chaotic Neural Network
4			@details Based on article description:
5			-
6
7			@authors Andrei Novikov ([email protected])
8			@date 2014-2016
9			@copyright GNU Public License
10
11			@cond GNU_PUBLIC_LICENSE
12			PyClustering is free software: you can redistribute it and/or modify
13			it under the terms of the GNU General Public License as published by
14			the Free Software Foundation, either version 3 of the License, or
15			(at your option) any later version.
16
17			PyClustering is distributed in the hope that it will be useful,
18			but WITHOUT ANY WARRANTY; without even the implied warranty of
19			MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
20			GNU General Public License for more details.
21
22			You should have received a copy of the GNU General Public License
23			along with this program. If not, see <http://www.gnu.org/licenses/>.
24			@endcond
25
26			"""
27
28			import matplotlib.pyplot as plt;
			0 ignored issues – show Configuration introduced 2016-06-21 15:55 UTC by Report Bug Copy Issue Report The import `matplotlib.pyplot` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
29			import matplotlib.animation as animation;
			0 ignored issues – show Configuration introduced 2016-06-21 15:55 UTC by Report Bug Copy Issue Report The import `matplotlib.animation` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history... Unused Code introduced 2016-06-21 15:55 UTC by Report Bug Copy Issue Report Unused matplotlib.animation imported as animation Loading history...
30
31			import math;
32			import random;
33			import numpy;
			0 ignored issues – show Configuration introduced 2016-06-21 15:55 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history... Unused Code introduced 2016-06-21 15:55 UTC by Report Bug Copy Issue Report The import `numpy` seems to be unused. Loading history...
34
35			from enum import IntEnum;
36
37			from pyclustering.utils import euclidean_distance_sqrt, average_neighbor_distance, heaviside, draw_dynamics;
38
39
40			class type_conn(IntEnum):
41			"""!
42			@brief Enumeration of connection types for Chaotic Neural Network.
43
44			@see cnn_network
45
46			"""
47
48			## All oscillators have connection with each other.
49			ALL_TO_ALL = 0,
50
51			## Connections between oscillators are created in line with Delaunay triangulation.
52			TRIANGULATION_DELAUNAY = 1,
53
54
55			class cnn_dynamic:
56			def __init__(self, output = [], time = []):
			0 ignored issues – show Bug Best Practice introduced 2016-06-21 12:49 UTC by Report Bug Copy Issue Report The default value `[]` might cause unintended side-effects. Objects as default values are only created once in Python and not on each invocation of the function. If the default object is modified, this modification is carried over to the next invocation of the method. # Bad: # If array_param is modified inside the function, the next invocation will # receive the modified object. def some_function(array_param=[]): # ... # Better: Create an array on each invocation def some_function(array_param=None): array_param = array_param or [] # ... Loading history...
57			self.output = output;
58			self.time = time;
59
60
61			def __len__(self):
62			return len(self.output);
63
64
65			def allocate_observation_matrix(self):
66			number_neurons = len(self.output[0]);
67			observation_matrix = [];
68
69			for iteration in range(len(self.output)):
70			obervation_column = [];
71			for index_neuron in range(number_neurons):
72			obervation_column.append(heaviside(self.output[iteration][index_neuron]));
73
74			observation_matrix.append(obervation_column);
75
76			return observation_matrix;
77
78
79			class cnn_visualizer:
80			@staticmethod
81			def show_output_dynamic(cnn_output_dynamic):
82			draw_dynamics(cnn_output_dynamic.time, cnn_output_dynamic.output, x_title = "t", y_title = "x");
83
84
85			@staticmethod
86			def show_dynamic_matrix(cnn_output_dynamic):
87			plt.imshow(cnn_output_dynamic.output, cmap = plt.get_cmap('gray'), interpolation='None', vmin = 0.0, vmax = 1.0);
88			plt.show();
89
90
91			@staticmethod
92			def show_observation_matrix(cnn_output_dynamic):
93			observation_matrix = cnn_output_dynamic.allocate_observation_matrix();
94			plt.imshow(observation_matrix, cmap = plt.get_cmap('gray'), interpolation='None', vmin = 0.0, vmax = 1.0);
95			plt.show();
96
97
98			class cnn_network:
99			def __init__(self, num_osc, conn_type = type_conn.ALL_TO_ALL, amount_neighbors = 3):
100			self.__num_osc = num_osc;
101			self.__conn_type = conn_type;
102			self.__amount_neighbors = amount_neighbors;
103
104			self.__average_distance = 0.0;
105			self.__weights = None;
106			self.__weights_summary = None;
107
108			self.__output = [ random.random() for _ in range(num_osc) ];
109
110
111			def __len__(self):
112			return self.__num_osc;
113
114
115			def simulate(self, steps, stimulus):
116			self.__create_weights(stimulus);
117
118			dynamic = cnn_dynamic([], []);
119			dynamic.output.append(self.__output);
120			dynamic.time.append(0);
121
122			for step in range(1, steps, 1):
123			self.__output = self.__calculate_states();
124
125			dynamic.output.append(self.__output);
126			dynamic.time.append(step);
127
128			return dynamic;
129
130
131			def __calculate_states(self):
132			output = [ 0.0 for _ in range(self.__num_osc) ];
133
134			for i in range(self.__num_osc):
135			output[i] = self.__neuron_evolution(i);
136
137			return output;
138
139
140			def __neuron_evolution(self, index):
141			value = 0.0;
142			#state = 1.0 - 2.0 * (self.__output[index] ** 2);
143
144			for index_neighbor in range(self.__num_osc):
145			value += self.__weights[index][index_neighbor] * (1.0 - 2.0 * (self.__output[index_neighbor] ** 2));
146
147			return value / self.__weights_summary[index];
148
149
150			def __create_weights(self, stimulus):
151			self.__average_distance = average_neighbor_distance(stimulus, self.__amount_neighbors);
152			print("Average distance: ", self.__average_distance);
153
154			self.__weights = [ [ 0.0 for _ in range(len(stimulus)) ] for _ in range(len(stimulus)) ];
155			self.__weights_summary = [ 0.0 for _ in range(self.__num_osc) ];
156
157			if (self.__conn_type == type_conn.ALL_TO_ALL):
158			self.__create_weights_all_to_all(stimulus);
159
160			elif (self.__conn_type == type_conn.TRIANGULATION_DELAUNAY):
161			self.__create_weights_delaunay_triangulation(stimulus);
162
163
164			def __create_weights_all_to_all(self, stimulus):
165			for i in range(len(stimulus)):
166			for j in range(i + 1, len(stimulus)):
167			weight = self.__calculate_weight(stimulus[i], stimulus[j]);
168			print(i, j, weight, stimulus[i], stimulus[j]);
169
170			self.__weights[i][j] = weight;
171			self.__weights[j][i] = weight;
172
173			self.__weights_summary[i] += weight;
174			self.__weights_summary[j] += weight;
175
176
177			def __create_weights_delaunay_triangulation(self, stimulus):
178			pass;
179
180
181			def __calculate_weight(self, oscillator_location1, oscillator_location2):
182			distance = euclidean_distance_sqrt(oscillator_location1, oscillator_location2);
183			return math.exp(-distance / (2.0 * self.__average_distance));
184

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