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