def show_winner_matrix(self):
"""!
@brief Show winner matrix where each element corresponds to neuron and value represents
amount of won objects from input dataspace at the last training iteration.
@see show_distance_matrix()
"""
if (self.__ccore_som_pointer is not None):
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
(fig, ax) = plt.subplots()
winner_matrix = [[0] * self._cols for i in range(self._rows)]
for i in range(self._rows):
for j in range(self._cols):
neuron_index = i * self._cols + j
winner_matrix[i][j] = self._award[neuron_index]
ax.text(i,
j,
str(winner_matrix[i][j]),
va='center',
ha='center')
ax.imshow(winner_matrix,
cmap=plt.get_cmap('cool'),
interpolation='none')
ax.grid(True)
plt.title("Winner Matrix")
plt.show()
def show_winner_matrix(self):
"""!
@brief Show winner matrix where each element corresponds to neuron and value represents
amount of won objects from input dataspace at the last training iteration.
@see show_distance_matrix()
"""
if self.__ccore_som_pointer is not None:
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
(fig, ax) = plt.subplots()
winner_matrix = [[0] * self._cols for i in range(self._rows)]
for i in range(self._rows):
for j in range(self._cols):
neuron_index = i * self._cols + j
winner_matrix[i][j] = self._award[neuron_index]
ax.text(i, j, str(winner_matrix[i][j]), va='center', ha='center')
ax.imshow(winner_matrix, cmap = plt.get_cmap('cool'), interpolation='none')
ax.grid(True)
plt.title("Winner Matrix")
plt.show()
def awards(self):
"""!
@return (list) Numbers of captured objects by each neuron.
"""
if (self.__ccore_som_pointer is not None):
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
return self._award
def awards(self):
"""!
@return (list) Numbers of captured objects by each neuron.
"""
if (self.__ccore_som_pointer is not None):
self._award = wrapper.som_get_awards(self.__ccore_som_pointer);
return self._award;
def awards(self):
"""!
@brief Return amount of captured objects by each neuron after training.
@return (list) Amount of captured objects by each neuron.
@see train()
"""
if self.__ccore_som_pointer is not None:
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
return self._award
def awards(self):
"""!
@brief Return amount of captured objects by each neuron after training.
@return (list) Amount of captured objects by each neuron.
@see train()
"""
if self.__ccore_som_pointer is not None:
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
return self._award
def get_winner_number(self):
"""!
@brief Calculates number of winner at the last step of learning process.
@return (uint) Number of winner.
"""
if (self.__ccore_som_pointer is not None):
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
winner_number = 0
for i in range(self._size):
if (self._award[i] > 0):
winner_number += 1
return winner_number
def get_winner_number(self):
"""!
@brief Calculates number of winner at the last step of learning process.
@return (uint) Number of winner.
"""
if self.__ccore_som_pointer is not None:
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
winner_number = 0
for i in range(self._size):
if self._award[i] > 0:
winner_number += 1
return winner_number
def show_network(self,
awards=False,
belongs=False,
coupling=True,
dataset=True,
marker_type='o'):
"""!
@brief Shows neurons in the dimension of data.
@param[in] awards (bool): If True - displays how many objects won each neuron.
@param[in] belongs (bool): If True - marks each won object by according index of neuron-winner (only when dataset is displayed too).
@param[in] coupling (bool): If True - displays connections between neurons (except case when function neighbor is used).
@param[in] dataset (bool): If True - displays inputs data set.
@param[in] marker_type (string): Defines marker that is used for dispaying neurons in the network.
"""
if (self.__ccore_som_pointer is not None):
self._size = wrapper.som_get_size(self.__ccore_som_pointer)
self._weights = wrapper.som_get_weights(self.__ccore_som_pointer)
self._neighbors = wrapper.som_get_neighbors(
self.__ccore_som_pointer)
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
dimension = len(self._weights[0])
fig = plt.figure()
axes = None
# Check for dimensions
if ((dimension == 1) or (dimension == 2)):
axes = fig.add_subplot(111)
elif (dimension == 3):
axes = fig.gca(projection='3d')
else:
raise NameError(
'Dwawer supports only 1D, 2D and 3D data representation')
# Show data
if ((self._data is not None) and (dataset is True)):
for x in self._data:
if (dimension == 1):
axes.plot(x[0], 0.0, 'b|', ms=30)
elif (dimension == 2):
axes.plot(x[0], x[1], 'b.')
elif (dimension == 3):
axes.scatter(x[0], x[1], x[2], c='b', marker='.')
# Show neurons
for index in range(self._size):
color = 'g'
if (self._award[index] == 0): color = 'y'
if (dimension == 1):
axes.plot(self._weights[index][0], 0.0, color + marker_type)
if (awards == True):
location = '{0}'.format(self._award[index])
axes.text(self._weights[index][0],
0.0,
location,
color='black',
fontsize=10)
if (belongs == True):
location = '{0}'.format(index)
axes.text(self._weights[index][0],
0.0,
location,
color='black',
fontsize=12)
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]]
axes.text(point[0],
0.0,
location,
color='blue',
fontsize=10)
if (dimension == 2):
axes.plot(self._weights[index][0], self._weights[index][1],
color + marker_type)
if (awards == True):
location = '{0}'.format(self._award[index])
axes.text(self._weights[index][0],
self._weights[index][1],
location,
color='black',
fontsize=10)
if (belongs == True):
location = '{0}'.format(index)
axes.text(self._weights[index][0],
self._weights[index][1],
location,
color='black',
fontsize=12)
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]]
axes.text(point[0],
point[1],
location,
color='blue',
fontsize=10)
if ((self._conn_type != type_conn.func_neighbor)
and (coupling != False)):
for neighbor in self._neighbors[index]:
if (neighbor > index):
axes.plot([
self._weights[index][0],
self._weights[neighbor][0]
], [
self._weights[index][1],
self._weights[neighbor][1]
],
'g',
linewidth=0.5)
elif (dimension == 3):
axes.scatter(self._weights[index][0],
self._weights[index][1],
self._weights[index][2],
c=color,
marker=marker_type)
if ((self._conn_type != type_conn.func_neighbor)
and (coupling != False)):
for neighbor in self._neighbors[index]:
if (neighbor > index):
axes.plot([
self._weights[index][0],
self._weights[neighbor][0]
], [
self._weights[index][1],
self._weights[neighbor][1]
], [
self._weights[index][2],
self._weights[neighbor][2]
],
'g-',
linewidth=0.5)
plt.title("Network Structure")
plt.grid()
plt.show()
def __download_dump_from_ccore(self):
self._location = self.__initialize_locations(self._rows, self._cols)
self._weights = wrapper.som_get_weights(self.__ccore_som_pointer)
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
self._capture_objects = wrapper.som_get_capture_objects(self.__ccore_som_pointer)
def __download_dump_from_ccore(self):
self._location = self.__initialize_locations(self._rows, self._cols)
self._weights = wrapper.som_get_weights(self.__ccore_som_pointer)
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
self._capture_objects = wrapper.som_get_capture_objects(self.__ccore_som_pointer)
def show_network(self, awards = False, belongs = False, coupling = True, dataset = True, marker_type = 'o'):
"""!
@brief Shows neurons in the dimension of data.
@param[in] awards (bool): If True - displays how many objects won each neuron.
@param[in] belongs (bool): If True - marks each won object by according index of neuron-winner (only when dataset is displayed too).
@param[in] coupling (bool): If True - displays connections between neurons (except case when function neighbor is used).
@param[in] dataset (bool): If True - displays inputs data set.
@param[in] marker_type (string): Defines marker that is used for dispaying neurons in the network.
"""
if self.__ccore_som_pointer is not None:
self._size = wrapper.som_get_size(self.__ccore_som_pointer)
self._weights = wrapper.som_get_weights(self.__ccore_som_pointer)
self._neighbors = wrapper.som_get_neighbors(self.__ccore_som_pointer)
self._award = wrapper.som_get_awards(self.__ccore_som_pointer)
dimension = len(self._weights[0])
fig = plt.figure()
# Check for dimensions
if (dimension == 1) or (dimension == 2):
axes = fig.add_subplot(111)
elif dimension == 3:
axes = fig.gca(projection='3d')
else:
raise NotImplementedError('Impossible to show network in data-space that is differ from 1D, 2D or 3D.')
if (self._data is not None) and (dataset is True):
for x in self._data:
if dimension == 1:
axes.plot(x[0], 0.0, 'b|', ms = 30)
elif dimension == 2:
axes.plot(x[0], x[1], 'b.')
elif dimension == 3:
axes.scatter(x[0], x[1], x[2], c = 'b', marker = '.')
# Show neurons
for index in range(self._size):
color = 'g'
if self._award[index] == 0:
color = 'y'
if dimension == 1:
axes.plot(self._weights[index][0], 0.0, color + marker_type)
if awards:
location = '{0}'.format(self._award[index])
axes.text(self._weights[index][0], 0.0, location, color='black', fontsize = 10)
if belongs and self._data is not None:
location = '{0}'.format(index)
axes.text(self._weights[index][0], 0.0, location, color='black', fontsize = 12)
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]]
axes.text(point[0], 0.0, location, color='blue', fontsize = 10)
if dimension == 2:
axes.plot(self._weights[index][0], self._weights[index][1], color + marker_type)
if awards:
location = '{0}'.format(self._award[index])
axes.text(self._weights[index][0], self._weights[index][1], location, color='black', fontsize=10)
if belongs and self._data is not None:
location = '{0}'.format(index)
axes.text(self._weights[index][0], self._weights[index][1], location, color='black', fontsize=12)
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]]
axes.text(point[0], point[1], location, color='blue', fontsize=10)
if (self._conn_type != type_conn.func_neighbor) and (coupling != False):
for neighbor in self._neighbors[index]:
if neighbor > index:
axes.plot([self._weights[index][0], self._weights[neighbor][0]],
[self._weights[index][1], self._weights[neighbor][1]],
'g', linewidth=0.5)
elif dimension == 3:
axes.scatter(self._weights[index][0], self._weights[index][1], self._weights[index][2], c=color, marker=marker_type)
if (self._conn_type != type_conn.func_neighbor) and (coupling != False):
for neighbor in self._neighbors[index]:
if neighbor > index:
axes.plot([self._weights[index][0], self._weights[neighbor][0]],
[self._weights[index][1], self._weights[neighbor][1]],
[self._weights[index][2], self._weights[neighbor][2]],
'g-', linewidth=0.5)
plt.title("Network Structure")
plt.grid()
plt.show()
def show_network(self, awards = False, belongs = False, coupling = True, dataset = True, marker_type = 'o'):
"""!
@brief Shows neurons in the dimension of data.
@param[in] awards (bool): If True - displays how many objects won each neuron.
@param[in] belongs (bool): If True - marks each won object by according index of neuron-winner (only when dataset is displayed too).
@param[in] coupling (bool): If True - displays connections between neurons (except case when function neighbor is used).
@param[in] dataset (bool): If True - displays inputs data set.
@param[in] marker_type (string): Defines marker that is used for dispaying neurons in the network.
"""
if (self.__ccore_som_pointer is not None):
self._size = wrapper.som_get_size(self.__ccore_som_pointer);
self._weights = wrapper.som_get_weights(self.__ccore_som_pointer);
self._neighbors = wrapper.som_get_neighbors(self.__ccore_som_pointer);
self._award = wrapper.som_get_awards(self.__ccore_som_pointer);
dimension = len(self._weights[0]);
fig = plt.figure();
axes = None;
# Check for dimensions
if ( (dimension == 1) or (dimension == 2) ):
axes = fig.add_subplot(111);
elif (dimension == 3):
axes = fig.gca(projection='3d');
else:
raise NameError('Dwawer supports only 1D, 2D and 3D data representation');
# Show data
if (dataset == True):
for x in self._data:
if (dimension == 1):
axes.plot(x[0], 0.0, 'b|', ms = 30);
elif (dimension == 2):
axes.plot(x[0], x[1], 'b.');
elif (dimension == 3):
axes.scatter(x[0], x[1], x[2], c = 'b', marker = '.');
# Show neurons
for index in range(self._size):
color = 'g';
if (self._award[index] == 0): color = 'y';
if (dimension == 1):
axes.plot(self._weights[index][0], 0.0, color + marker_type);
if (awards == True):
location = '{0}'.format(self._award[index]);
axes.text(self._weights[index][0], 0.0, location, color='black', fontsize = 10);
if (belongs == True):
location = '{0}'.format(index);
axes.text(self._weights[index][0], 0.0, location, color='black', fontsize = 12);
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]];
axes.text(point[0], 0.0, location, color='blue', fontsize = 10);
if (dimension == 2):
axes.plot(self._weights[index][0], self._weights[index][1], color + marker_type);
if (awards == True):
location = '{0}'.format(self._award[index]);
axes.text(self._weights[index][0], self._weights[index][1], location, color='black', fontsize = 10);
if (belongs == True):
location = '{0}'.format(index);
axes.text(self._weights[index][0], self._weights[index][1], location, color='black', fontsize = 12);
for k in range(len(self._capture_objects[index])):
point = self._data[self._capture_objects[index][k]];
axes.text(point[0], point[1], location, color='blue', fontsize = 10);
if ( (self._conn_type != type_conn.func_neighbor) and (coupling != False) ):
for neighbor in self._neighbors[index]:
if (neighbor > index):
axes.plot([self._weights[index][0], self._weights[neighbor][0]], [self._weights[index][1], self._weights[neighbor][1]], 'g', linewidth = 0.5);
elif (dimension == 3):
axes.scatter(self._weights[index][0], self._weights[index][1], self._weights[index][2], c = color, marker = marker_type);
if ( (self._conn_type != type_conn.func_neighbor) and (coupling != False) ):
for neighbor in self._neighbors[index]:
if (neighbor > index):
axes.plot([self._weights[index][0], self._weights[neighbor][0]], [self._weights[index][1], self._weights[neighbor][1]], [self._weights[index][2], self._weights[neighbor][2]], 'g-', linewidth = 0.5);
plt.title("Network Structure");
plt.grid();
plt.show();
