# Network¶

## Weight Patterns¶

 save_patterns(h5_file, items, **kwargs) Write patterns and similarity matrices to hdf5. load_patterns(h5_file[, features]) Load weights from an hdf5 file. prepare_patterns(patterns, weights) Scale and concatenate item patterns and connections. unpack_weights(weight_template, weight_param) Apply parameter values to a weight template.

## Network Initialization¶

 Network(f_segment, c_segment) Representation of interacting item and context layers.

## Accessing and Setting Values¶

 Copy the network to a new network object. Reset network weights and activations to zero. Network.get_sublayer(layer, sublayer) Get indices for a sublayer. Network.get_sublayers(layer, sublayers) Get an array of indices for multiple sublayers. Network.get_segment(layer, sublayer, segment) Get indices for a segment. Network.get_unit(layer, sublayer, segment, unit) Get indices for a unit. Network.add_pre_weights(connect, f_segment, …) Add pre-experimental weights to a network.

## Operations¶

 Network.update(item, sublayers) Update context completely with input from the item layer. Network.integrate(item, sublayers, B) Integrate input from the item layer into context. Network.present(item, sublayers, B[, Lfc, Lcf]) Present an item and learn context-item associations. Network.learn(connect, item, sublayers, L) Learn an association between the item and context layers.

 Network.study(segment, item_list, sublayers, …) Study a list of items. Network.p_recall(segment, recalls, …[, amin]) Calculate the probability of a specific recall sequence. Network.generate_recall(segment, sublayers, …) Generate a sequence of simulated free recall events. Network.generate_recall_lba(segment, …) Generate timed free recall using the LBA model.
 Network.record_study(segment, item_list, …) Network.record_recall(segment, recalls, …) Network.plot([ax]) Plot the current state of the network.