reservoirpy.compat.ESNOnline#

class reservoirpy.compat.ESNOnline(lr, W, Win, dim_out, alpha_coef=1e-06, use_raw_input=False, input_bias=True, Wfb=None, fbfunc=None, typefloat=<class 'numpy.float64'>)[source]#

Echo State Networks using FORCE algorithm as an online learning rule.

Warning

The v0.2 model compat.ESNOnline is deprecated. Consider using the new Node API introduced in v0.3 (see Node functional API).

The compat.ESNOnline implements FORCE, an online learning method. Online Echo State Networks allow one to: - quickly build online ESNs, using the reservoirpy.mat_gen module to initialize weights, - train and test ESNs on the task of your choice in an online fashion, i.e. continuously in time.

Parameters:

  • lr (float) – Leaking rate

  • W (np.ndarray) – Reservoir weights matrix

  • Win (np.ndarray) – Input weights matrix

  • Wout (np.ndarray) – Readout weights matrix

  • alpha_coef (float, optional) – Coefficient to scale the inverted state correlation matrix used for FORCE learning. By default, equal to \(1e^{-6}\).

  • use_raw_input (bool, optional) – If True, input is used directly when computing output. By default, is False.

  • input_bias (bool, optional) – If True, will add a constant bias to the input vector. By default, True.

  • Wfb (np.array, optional) – Feedback weights matrix.

  • fbfunc (Callable, optional) – Feedback activation function.

  • typefloat (numpy.dtype, optional) –

Wout#

Readout matrix

Type:

np.ndarray

dim_out#

Output dimension

Type:

int

dim_in#

Input dimension

Type:

int

N#

Number of neuronal units

Type:

int

state#

Last internal state computed, used to store internal dynamics of the reservoir over time, to enable online learning.

Type:

numpy.ndarray

output_values#

Last values predicted by the network, used to store the last response of the reservoir to enable online learning.

Type:

numpy.ndarray

state_corr_inv#

Inverse correlation matrix used in FORCE learning algorithm.

Type:

numpy.ndarray

Methods

__init__(lr, W, Win, dim_out[, alpha_coef, ...])

compute_output(single_input[, wash_nr_time_step])

Compute output from input to the reservoir.

compute_output_from_current_state()

Compute output from current state s(t) of the reservoir.

reset_correlation_matrix()

Reset inverse correlation state matrix to the initial value :

reset_reservoir()

Reset reservoir by setting internal values to zero.

reset_state()

Reset reservoir by setting internal values to zero.

run(inputs[, verbose])

Run the model on a sequence of inputs, and returned the states and

save(directory)

Save the ESN to disk.

train(inputs, teachers[, wash_nr_time_step, ...])

Train the ESN model on a sequence of inputs.

train_from_current_state(targeted_output[, ...])

Train Wout from current internal state.
compute_output(single_input, wash_nr_time_step=0)[source]#

Compute output from input to the reservoir.

Parameters:

  • single_input (np.ndarray) – Input vector u(t)

  • wash_nr_time_step (int, optional) – Time for reservoir to run without collecting output or fitting Wout. (default, 0)

Returns:

New state after input u(t) is passed and output after input u(t) is passed

Return type:

np.ndarray, np.ndarray

compute_output_from_current_state()[source]#

Compute output from current state s(t) of the reservoir.

Returns:

Output at time t.

Return type:

np.ndarray

reset_correlation_matrix()[source]#

Reset inverse correlation state matrix to the initial value :

\[Corr^{inv} = Id_{N} * \alpha\]

where \(\alpha\) is the alpha_coef and \(N\) is the number of units in the reservoir.

reset_reservoir()[source]#

Reset reservoir by setting internal values to zero.

reset_state()[source]#

Reset reservoir by setting internal values to zero.

run(inputs, verbose=False)[source]#
Run the model on a sequence of inputs, and returned the states and

readouts vectors.

Parameters:

  • inputs (list of numpy.ndarray) – List of inputs. Note that it should always be a list of sequences, i.e. if only one sequence (array with rows representing time axis) of inputs is used, it should be alone in a list.

  • verbose (bool) –

Returns:

All outputs computed from readout and all corresponding internal states, for all inputs.

Return type:

list of numpy.ndarray, list of numpy.ndarray

save(directory)[source]#

Save the ESN to disk.

Parameters:

directory (str or Path) – Directory where to save the model..

train(inputs, teachers, wash_nr_time_step=0, verbose=False)[source]#

Train the ESN model on a sequence of inputs.

Parameters:

  • inputs (list of numpy.ndarray) – List of inputs. Note that it should always be a list of sequences, i.e. if only one sequence (array with rows representing time axis) of inputs is used, it should be alone in a list.

  • teachers (list of numpy.ndarray) – List of ground truths. Note that is should always be a list of sequences of the same length than the inputs, i.e. if only one sequence of inputs is used, it should be alone in a list.

  • wash_nr_time_step (int, optional) – Number of states to considered as transient when training. Transient states will be discarded when computing readout matrix. By default, no states are removes.

  • verbose (bool) –

Returns:

All states computed, for all inputs.

Return type:

list of np.ndarray

train_from_current_state(targeted_output, indexes=None)[source]#

Train Wout from current internal state.

Parameters:

  • targeted_output (np.ndarray) – Expected output of the ESN.

  • indexes (int or list, optional) – If indexes is not None, only the provided output indexes are learned.