def train(self, autostop = False):
"""!
@brief Trains self-organized feature map (SOM).
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
if (self.__ccore_som_pointer is not None):
return wrapper.som_train(self.__ccore_som_pointer, autostop);
previous_weights = None;
for epoch in range(1, self._epochs + 1):
# Depression term of coupling
self._local_radius = ( self._params.init_radius * math.exp(-(epoch / self._epochs)) ) ** 2;
self._learn_rate = self._params.init_learn_rate * math.exp(-(epoch / self._epochs));
#random.shuffle(self._data); # Random order
# Feature SOM 0003: Clear statistics
if (autostop == True):
for i in range(self._size):
self._award[i] = 0;
self._capture_objects[i].clear();
for i in range(len(self._data)):
# Step 1: Competition:
index = self._competition(self._data[i]);
# Step 2: Adaptation:
self._adaptation(index, self._data[i]);
# Update statistics
if ( (autostop == True) or (epoch == (self._epochs - 1)) ):
self._award[index] += 1;
self._capture_objects[index].append(i);
# Feature SOM 0003: Check requirement of stopping
if (autostop == True):
if (previous_weights is not None):
maximal_adaptation = self._get_maximal_adaptation(previous_weights);
if (maximal_adaptation < self._params.adaptation_threshold):
return epoch;
previous_weights = [item[:] for item in self._weights];
return self._epochs;
def train(self, autostop = False):
"""!
@brief Trains self-organized feature map (SOM).
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
if (self.__ccore_som_pointer is not None):
return wrapper.som_train(self.__ccore_som_pointer, autostop);
previous_weights = None;
for epoch in range(1, self._epochs + 1):
# Depression term of coupling
self._local_radius = ( self._params.init_radius * math.exp(-(epoch / self._epochs)) ) ** 2;
self._learn_rate = self._params.init_learn_rate * math.exp(-(epoch / self._epochs));
#random.shuffle(self._data); # Random order
# Feature SOM 0003: Clear statistics
if (autostop == True):
for i in range(self._size):
self._award[i] = 0;
self._capture_objects[i].clear();
for i in range(len(self._data)):
# Step 1: Competition:
index = self._competition(self._data[i]);
# Step 2: Adaptation:
self._adaptation(index, self._data[i]);
# Update statistics
if ( (autostop == True) or (epoch == (self._epochs - 1)) ):
self._award[index] += 1;
self._capture_objects[index].append(i);
# Feature SOM 0003: Check requirement of stopping
if (autostop == True):
if (previous_weights is not None):
maximal_adaptation = self._get_maximal_adaptation(previous_weights);
if (maximal_adaptation < self._params.adaptation_threshold):
return epoch;
previous_weights = [item[:] for item in self._weights];
return self._epochs;
def train(self, data, epochs, autostop=False):
"""!
@brief Trains self-organized feature map (SOM).
@param[in] data (list): Input data - list of points where each point is represented by list of features, for example coordinates.
@param[in] epochs (uint): Number of epochs for training.
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
self._data = data
if (self.__ccore_som_pointer is not None):
return wrapper.som_train(self.__ccore_som_pointer, data, epochs,
autostop)
for i in range(self._size):
self._award[i] = 0
self._capture_objects[i].clear()
# weights
self._create_initial_weights(self._params.init_type)
previous_weights = None
for epoch in range(1, epochs + 1):
# Depression term of coupling
self._local_radius = (self._params.init_radius *
math.exp(-(epoch / epochs)))**2
self._learn_rate = self._params.init_learn_rate * math.exp(
-(epoch / epochs))
#random.shuffle(self._data); # Random order
# Feature SOM 0003: Clear statistics
if (autostop == True):
for i in range(self._size):
self._award[i] = 0
self._capture_objects[i].clear()
for i in range(len(self._data)):
# Step 1: Competition:
index = self._competition(self._data[i])
# Step 2: Adaptation:
self._adaptation(index, self._data[i])
# Update statistics
if ((autostop == True) or (epoch == epochs)):
self._award[index] += 1
self._capture_objects[index].append(i)
# Feature SOM 0003: Check requirement of stopping
if (autostop == True):
if (previous_weights is not None):
maximal_adaptation = self._get_maximal_adaptation(
previous_weights)
if (maximal_adaptation <
self._params.adaptation_threshold):
return epoch
previous_weights = [item[:] for item in self._weights]
return epochs
def train(self, data, epochs, autostop=False):
"""!
@brief Trains self-organized feature map (SOM).
@param[in] data (list): Input data - list of points where each point is represented by list of features, for example coordinates.
@param[in] epochs (uint): Number of epochs for training.
@param[in] autostop (bool): Automatic termination of learining process when adaptation is not occurred.
@return (uint) Number of learining iterations.
"""
self._data = data
if self.__ccore_som_pointer is not None:
return wrapper.som_train(self.__ccore_som_pointer, data, epochs, autostop)
self._sqrt_distances = self.__initialize_distances(self._size, self._location)
for i in range(self._size):
self._award[i] = 0
self._capture_objects[i].clear()
# weights
self._create_initial_weights(self._params.init_type)
previous_weights = None
for epoch in range(1, epochs + 1):
# Depression term of coupling
self._local_radius = (self._params.init_radius * math.exp(-(epoch / epochs))) ** 2
self._learn_rate = self._params.init_learn_rate * math.exp(-(epoch / epochs))
# Clear statistics
if autostop:
for i in range(self._size):
self._award[i] = 0
self._capture_objects[i].clear()
for i in range(len(self._data)):
# Step 1: Competition:
index = self._competition(self._data[i])
# Step 2: Adaptation:
self._adaptation(index, self._data[i])
# Update statistics
if (autostop == True) or (epoch == epochs):
self._award[index] += 1
self._capture_objects[index].append(i)
# Check requirement of stopping
if autostop:
if previous_weights is not None:
maximal_adaptation = self._get_maximal_adaptation(previous_weights)
if maximal_adaptation < self._params.adaptation_threshold:
return epoch
previous_weights = [item[:] for item in self._weights]
return epochs