コード例 #1
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    def random_state(rows, cols, connections, random_state, ccore_flag):
        sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)

        params = som_parameters()
        params.random_state = random_state

        network_1 = som(rows, cols, connections, ccore=ccore_flag)
        steps_1 = network_1.train(sample, 100, True)

        network_2 = som(rows, cols, connections, ccore=ccore_flag)
        steps_2 = network_2.train(sample, 100, True)

        assert steps_1 == steps_2
        assert network_1.weights == network_2.weights
        assert network_1.capture_objects == network_2.capture_objects
        assert network_1.awards == network_2.awards
コード例 #2
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    def templateTestAwardNeurons(file, rows, cols, time, expected_result, autostop, ccore_flag, parameters = None, **kwargs):
        store_load = kwargs.get('store_load', False)

        types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
        sample = read_sample(file)
         
        if (parameters is None):
            parameters = som_parameters()
         
        for stucture in types:
            network = som(rows, cols, stucture, parameters, ccore = ccore_flag)
            if store_load:
                dump_network = pickle.dumps(network)
                network = pickle.loads(dump_network)

            network.train(sample, time, autostop)
            
            winners = network.get_winner_number()
            assert winners == len(expected_result)
            
            if sorted(network.awards) != expected_result:
                network.show_network(awards = True)
                assert sorted(network.awards) == expected_result
             
            total_capture_points = 0
            for points in network.capture_objects:
                total_capture_points += len(points)
             
            assert total_capture_points == sum(expected_result)
         
            del network
コード例 #3
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    def templateTestAwardNeurons(file, rows, cols, time, expected_result, autostop, ccore_flag, parameters = None, **kwargs):
        store_load = kwargs.get('store_load', False)

        types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
        sample = read_sample(file)
         
        if (parameters is None):
            parameters = som_parameters()
         
        for stucture in types:
            network = som(rows, cols, stucture, parameters, ccore = ccore_flag)
            if store_load:
                dump_network = pickle.dumps(network)
                network = pickle.loads(dump_network)

            network.train(sample, time, autostop)
            
            winners = network.get_winner_number()
            assert winners == len(expected_result)
            
            if sorted(network.awards) != expected_result:
                network.show_network(awards = True)
                assert sorted(network.awards) == expected_result
             
            total_capture_points = 0
            for points in network.capture_objects:
                total_capture_points += len(points)
             
            assert total_capture_points == sum(expected_result)
         
            del network
コード例 #4
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 def train(self):
     samples = [];
     
     print("Digit images preprocessing...");
     
     for index_digit in range(0, 10, 1):
         list_file_digit_sample = IMAGE_DIGIT_SAMPLES.GET_LIST_IMAGE_SAMPLES(index_digit);
         
         for file_name in list_file_digit_sample:
             data = read_image(file_name);
             
             image_pattern = rgb2gray(data);
             
             for index_pixel in range(len(image_pattern)):
                 if (image_pattern[index_pixel] < 128):
                     image_pattern[index_pixel] = 1;
                 else:
                     image_pattern[index_pixel] = 0;
             
             samples += [ image_pattern ];
     
    
     print("SOM initialization...");
     self.__network = som(2, 5, type_conn.grid_four, None, True);
     
     print("SOM training...");
     self.__network.train(samples, 300);
     
     print("SOM is ready...");
コード例 #5
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def template_self_organization(file,
                               rows,
                               cols,
                               time,
                               structure,
                               init_type=None,
                               init_radius=None,
                               init_rate=None,
                               umatrix=False,
                               pmatrix=False,
                               awards=False):
    parameters = som_parameters()

    if (init_type is not None):
        parameters.init_type = init_type
    if (init_radius is not None):
        parameters.init_radius = init_radius
    if (init_rate is not None):
        parameters.init_learn_rate = init_rate

    sample = read_sample(file)
    network = som(rows, cols, structure, parameters, True)
    network.train(sample, time)
    network.show_network(False, dataset=False)

    if (umatrix is True):
        network.show_distance_matrix()

    if (pmatrix is True):
        network.show_density_matrix()

    if (awards is True):
        network.show_winner_matrix()
コード例 #6
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ファイル: syncsom.py プロジェクト: xdwwb/pyclustering
    def __init__(self, data, rows, cols, radius):
        """!
        @brief Constructor of the double layer oscillatory network SYNC-SOM.
        
        @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
        @param[in] rows (uint): Rows of neurons (number of neurons in column) in the input layer (self-organized feature map).
        @param[in] cols (uint): Columns of neurons (number of neurons in row) in the input later (self-organized feature map).
        @param[in] radius (double): Connectivity radius between objects that defines connection between oscillators in the second layer.
        
        """

        self._data = data
        self._radius = radius * radius

        self._som = som(rows, cols, conn_type=type_conn.grid_four, ccore=False)
        # The first (input) later - SOM layer.
        self._som_osc_table = list()

        self._sync = None
        # The second (output) layer - Sync layer.
        self._struct = None
        # Structure of connections between oscillators in the second layer - Sync layer.

        # For convenience
        self._analyser = None
コード例 #7
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ファイル: som_recognition.py プロジェクト: Gudui/pyclustering
 def train(self):
     samples = [];
     
     print("Digit images preprocessing...");
     
     for index_digit in range(0, 10, 1):
         list_file_digit_sample = IMAGE_DIGIT_SAMPLES.GET_LIST_IMAGE_SAMPLES(index_digit);
         
         for file_name in list_file_digit_sample:
             data = read_image(file_name);
             
             image_pattern = rgb2gray(data);
             
             for index_pixel in range(len(image_pattern)):
                 if (image_pattern[index_pixel] < 128):
                     image_pattern[index_pixel] = 1;
                 else:
                     image_pattern[index_pixel] = 0;
             
             samples += [ image_pattern ];
     
    
     print("SOM initialization...");
     self.__network = som(2, 5, type_conn.grid_four, None, True);
     
     print("SOM training...");
     self.__network.train(samples, 300);
     
     print("SOM is ready...");
コード例 #8
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    def templateTestAwardNeurons(self,
                                 file,
                                 rows,
                                 cols,
                                 time,
                                 expected_result,
                                 autostop=False,
                                 ccore_flag=False,
                                 parameters=None):
        types = [
            type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four,
            type_conn.honeycomb
        ]
        sample = read_sample(file)

        if (parameters is None):
            parameters = som_parameters()

        for stucture in types:
            network = som(rows, cols, stucture, parameters, ccore=ccore_flag)
            network.train(sample, time, autostop)

            if (sorted(network.awards) != expected_result):
                network.show_network(awards=True)
                print(sorted(network.awards))
                assert sorted(network.awards) == expected_result

            total_capture_points = 0
            for points in network.capture_objects:
                total_capture_points += len(points)

            assert total_capture_points == sum(expected_result)

            del network
コード例 #9
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ファイル: it_som.py プロジェクト: annoviko/pyclustering
 def testSomVisualizationByCore(self):
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)
     
     parameters = som_parameters()
     network = som(5, 5, type_conn.grid_eight, parameters, ccore = True)
     network.train(sample, 100, True)
     
     network.show_network()
     network.show_winner_matrix()
     network.show_distance_matrix()
     network.show_density_matrix()
コード例 #10
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    def testSomVisualizationByCore(self):
        sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE4)

        parameters = som_parameters()
        network = som(5, 5, type_conn.grid_eight, parameters, ccore=True)
        network.train(sample, 100, True)

        network.show_network()
        network.show_winner_matrix()
        network.show_distance_matrix()
        network.show_density_matrix()
コード例 #11
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ファイル: somsc.py プロジェクト: annoviko/pyclustering
 def process(self):
     """!
     @brief Performs cluster analysis by competition between neurons of SOM.
     
     @remark Results of clustering can be obtained using corresponding get methods.
     
     @see get_clusters()
     
     """
     
     self.__network = som(1, self.__amount_clusters, type_conn.grid_four, None, self.__ccore);
     self.__network.train(self.__data_pointer, self.__epouch, True);
コード例 #12
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ファイル: somsc.py プロジェクト: rockanandu/pyclustering
 def process(self):
     """!
     @brief Performs cluster analysis by competition between neurons of SOM.
     
     @remark Results of clustering can be obtained using corresponding get methods.
     
     @see get_clusters()
     
     """
     
     self.__network = som(1, self.__amount_clusters, type_conn.grid_four, None, self.__ccore);
     self.__network.train(self.__data_pointer, self.__epouch, True);
コード例 #13
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ファイル: syncsom.py プロジェクト: alishakiba/pyclustering
 def __init__(self, data, rows, cols):
     """!
     @brief Constructor of the double layer oscillatory network SYNC-SOM.
     
     @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
     @param[in] rows (uint): Rows of neurons (number of neurons in column) in the input layer (self-organized feature map).
     @param[in] cols (uint): Columns of neurons (number of neurons in row) in the input later (self-organized feature map).
     
     """
     
     self._data = data;
     self._som = som(rows, cols, data, 100, conn_type = type_conn.grid_four);
     self._som_osc_table = list();        
コード例 #14
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ファイル: syncsom.py プロジェクト: weihuang0908/pyclustering
    def __init__(self, data, rows, cols):
        """!
        @brief Constructor of the double layer oscillatory network SYNC-SOM.
        
        @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
        @param[in] rows (uint): Rows of neurons (number of neurons in column) in the input layer (self-organized feature map).
        @param[in] cols (uint): Columns of neurons (number of neurons in row) in the input later (self-organized feature map).
        
        """

        self._data = data
        self._som = som(rows, cols, data, 100, conn_type=type_conn.grid_four)
        self._som_osc_table = list()
コード例 #15
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 def process(self):
     """!
     @brief Performs cluster analysis by competition between neurons of SOM.
     
     @return (somsc) Returns itself (SOM Simple Clustering instance).
     
     @see get_clusters()
     
     """
     
     self.__network = som(1, self.__amount_clusters, type_conn.grid_four, None, self.__ccore)
     self.__network.train(self.__data_pointer, self.__epouch, True)
     return self
コード例 #16
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ファイル: ut_som.py プロジェクト: RishabGoel/pyclustering
 def testDoubleTrain(self):
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
     
     parameters = som_parameters();
     network = som(2, 2, type_conn.grid_eight, parameters, ccore = False);
      
     network.train(sample, 100, False);
     network.train(sample, 100, False);
      
     assert sum(network.awards) == len(sample);
      
     total_capture_points = 0;
     for points in network.capture_objects:
         total_capture_points += len(points);
      
     assert total_capture_points == len(sample);
コード例 #17
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ファイル: som_tests.py プロジェクト: Gudui/pyclustering
 def testDoubleTrain(self):
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
     
     parameters = som_parameters();
     network = som(2, 2, type_conn.grid_eight, parameters, ccore = False);
     
     network.train(sample, 100, False);
     network.train(sample, 100, False);
     
     assert sum(network.awards) == len(sample);
     
     total_capture_points = 0;
     for points in network.capture_objects:
         total_capture_points += len(points);
     
     assert total_capture_points == len(sample);       
コード例 #18
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 def templateTestSimulate(connections, ccore_flag):
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1);
     
     network = som(1, 2, connections, ccore = ccore_flag);
     network.train(sample, 100);
     
     expected_winners = [0, 1];
     for i in range(len(sample)):
         index_winner = network.simulate(sample[i]);
         if ( (i == 0) and (index_winner == 1)):
             expected_winners = [1, 0];
         
         if (i < 5):
             assert expected_winners[0] == index_winner;
         else:
             assert expected_winners[1] == index_winner;
コード例 #19
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 def templateTestAwardNeurons(self, file, rows, cols, time, expected_result, autostop = False, ccore_flag = False):
     types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb];
     sample = read_sample(file);
     
     for stucture in types:
         network = som(rows, cols, sample, time, stucture, ccore = ccore_flag); 
         network.train(autostop);
             
         assert sorted(network.awards) == expected_result;
         
         total_capture_points = 0;
         for points in network.capture_objects:
             total_capture_points += len(points);
             
         assert total_capture_points == sum(expected_result);
     
         del network;
コード例 #20
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    def process(self):
        """!
        @brief Performs cluster analysis by competition between neurons in self-organized map.
        
        @return (somsc) Returns itself (SOM Simple Clustering instance).
        
        @see get_clusters()
        
        """

        params = som_parameters()
        params.random_state = self.__random_state

        self.__network = som(1, self.__amount_clusters, type_conn.grid_four,
                             params, self.__ccore)
        self.__network.train(self.__data_pointer, self.__epouch, True)

        return self
コード例 #21
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 def templateTestWinners(ccore_flag):
     types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
             
     for stucture in types:
         network = som(5, 5, stucture, ccore = ccore_flag)
         network.train(sample, 100)
                 
         assert sum(network.awards) == 60
                 
         points = list()
         for i in range(network.size):
             if network.awards[i] > 0:
                 points += network.capture_objects[i]
                 
         assert len(points) == len(sample)
                 
         points = sorted(points)
         for i in range(len(points)):
             assert points[i] == i
コード例 #22
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 def templateTestWinners(ccore_flag):
     types = [type_conn.func_neighbor, type_conn.grid_eight, type_conn.grid_four, type_conn.honeycomb]
     sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)
             
     for stucture in types:
         network = som(5, 5, stucture, ccore = ccore_flag)
         network.train(sample, 100)
                 
         assert sum(network.awards) == 60
                 
         points = list()
         for i in range(network.size):
             if network.awards[i] > 0:
                 points += network.capture_objects[i]
                 
         assert len(points) == len(sample)
                 
         points = sorted(points)
         for i in range(len(points)):
             assert points[i] == i
コード例 #23
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    def templateTestSimulate(connections, ccore_flag, **kwargs):
        store_load = kwargs.get('store_load', False)

        sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
        
        network = som(1, 2, connections, ccore = ccore_flag)
        network.train(sample, 100)

        if store_load:
            dump_network = pickle.dumps(network)
            network = pickle.loads(dump_network)

        expected_winners = [0, 1]
        for i in range(len(sample)):
            index_winner = network.simulate(sample[i])
            if (i == 0) and (index_winner == 1):
                expected_winners = [1, 0]
            
            if i < 5:
                assert expected_winners[0] == index_winner
            else:
                assert expected_winners[1] == index_winner
コード例 #24
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    def templateTestSimulate(connections, ccore_flag, **kwargs):
        store_load = kwargs.get('store_load', False)

        sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE1)
        
        network = som(1, 2, connections, ccore = ccore_flag)
        network.train(sample, 100)

        if store_load:
            dump_network = pickle.dumps(network)
            network = pickle.loads(dump_network)

        expected_winners = [0, 1]
        for i in range(len(sample)):
            index_winner = network.simulate(sample[i])
            if (i == 0) and (index_winner == 1):
                expected_winners = [1, 0]
            
            if i < 5:
                assert expected_winners[0] == index_winner
            else:
                assert expected_winners[1] == index_winner
コード例 #25
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ファイル: syncsom.py プロジェクト: annoviko/pyclustering
 def __init__(self, data, rows, cols, radius):
     """!
     @brief Constructor of the double layer oscillatory network SYNC-SOM.
     
     @param[in] data (list): Input data that is presented as list of points (objects), each point should be represented by list or tuple.
     @param[in] rows (uint): Rows of neurons (number of neurons in column) in the input layer (self-organized feature map).
     @param[in] cols (uint): Columns of neurons (number of neurons in row) in the input later (self-organized feature map).
     @param[in] radius (double): Connectivity radius between objects that defines connection between oscillators in the second layer.
     
     """
     
     self._data = data;
     self._radius = radius * radius;
     
     self._som = som(rows, cols, conn_type = type_conn.grid_four, ccore = False);   # The first (input) later - SOM layer.
     self._som_osc_table = list();
     
     self._sync = None;       # The second (output) layer - Sync layer.
     self._struct = None;     # Structure of connections between oscillators in the second layer - Sync layer.
     
     # For convenience
     self._analyser = None;
コード例 #26
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ファイル: som_examples.py プロジェクト: annoviko/pyclustering
def template_self_organization(file, rows, cols, time, structure, init_type = None, init_radius = None, init_rate = None, umatrix = False, pmatrix = False, awards = False):
    parameters = som_parameters();
    
    if (init_type is not None):
        parameters.init_type = init_type;
    if (init_radius is not None):
        parameters.init_radius = init_radius;
    if (init_rate is not None):
        parameters.init_learn_rate = init_rate;
    
    sample = read_sample(file);
    network = som(rows, cols, structure, parameters, True);
    network.train(sample, time);
    network.show_network(False, dataset = False);
    
    if (umatrix is True):
        network.show_distance_matrix();
        
    if (pmatrix is True): 
        network.show_density_matrix();
    
    if (awards is True):
        network.show_winner_matrix();
コード例 #27
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                    img_org.item(y, x, 2)])

print('selected_pixel_bgr :\n', bgr_val)


## 5. SOM
sample = bgr_val

# create SOM parameters
parameters = som_parameters() # initialization of initial neuron weights, radius, rateOfLearning, autostop

# create self-organized feature map with size 7x7
rows = 6  # five rows
cols = 6  # five columns
structure = type_conn.honeycomb  # each neuron has max. four neighbors.
network = som(rows, cols, structure, parameters)


# train network on sample during 100 epochs.
network.train(sample, 50)

# simulate trained network using randomly modified point from input dataset.
index_point = random.randint(0, len(sample) - 1)
point = sample[index_point]  # obtain randomly point from data
point[0] += random.random() * 0.2  # change randomly X-coordinate
point[1] += random.random() * 0.2  # change randomly Y-coordinate
index_winner = network.simulate(point)

# check what are objects from input data are much close to randomly modified.
index_similar_objects = network.capture_objects[index_winner]