Module ImportFrom alias ImportFrom alias alias alias alias alias alias alias ImportFrom alias ImportFrom alias alias ImportFrom alias Assign NameStore TupleLoad Str ClassDef bases NameLoad body Expr Str Assign NameStore Call NameLoad keyword Num Assign NameStore Call NameLoad keyword Num keyword Num FunctionDef arguments args NameParam NameParam defaults body Assign NameStore Call NameLoad NameLoad If CompareNotEq AttributeLoad NameLoad attr Num body Raise Call NameLoad Call AttributeLoad Str attr AttributeLoad NameLoad attr Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr If CompareNotEq Call NameLoad Call NameLoad Call NameLoad NameLoad ListLoad Num Num body Raise Call NameLoad Str If UnaryOpNot Call NameLoad NameLoad Str body Assign AttributeStore NameLoad attr Call NameLoad TupleLoad NameLoad NameLoad If UnaryOpNot Call NameLoad NameLoad Str body Assign AttributeStore NameLoad attr BinOpMult BinOpDiv NameLoad BinOpSub BinOpAdd NameLoad NameLoad Num AttributeLoad AttributeLoad NameLoad attr attr Assign NameStore AttributeLoad NameLoad attr Assign NameStore AttributeLoad NameLoad attr If CompareNotEq NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num body Raise Call NameLoad Call AttributeLoad Str attr NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num For TupleStore NameStore NameStore Call NameLoad NameLoad body Assign NameStore ListLoad Assign NameStore ListLoad Assign NameStore NameLoad While NameLoad body Assign NameStore NameLoad Assign NameStore Call NameLoad NameLoad AttributeLoad NameLoad attr Assign NameStore Call NameLoad AttributeLoad NameLoad attr Assign SubscriptStore NameLoad Index NameLoad UnaryOpUSub NameLoad Assign NameStore Call AttributeLoad NameLoad attr Assign SubscriptStore NameLoad Index NameLoad Num Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore Call AttributeLoad Call NameLoad NameLoad NameLoad attr NameLoad Assign NameStore BinOpDiv Call NameLoad AttributeLoad NameLoad attr NameLoad Call NameLoad AttributeLoad NameLoad attr NameLoad Assign NameStore CompareLt NameLoad NameLoad If NameLoad body Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad If CompareGtE Call NameLoad NameLoad NameLoad body Assign NameStore NameLoad Assign NameStore NameLoad If UnaryOpNot NameLoad body If CompareIsNot NameLoad NameLoad body Assign SubscriptStore NameLoad ExtSlice Index NameLoad Slice BinOpDiv BinOpMult NameLoad NameLoad BinOpSub BinOpAdd NameLoad Call NameLoad AttributeLoad NameLoad attr NameLoad Num Assign SubscriptStore NameLoad ExtSlice Slice Index NameLoad NameLoad orelse Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign SubscriptStore NameLoad Index NameLoad NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Return Call AttributeLoad NameLoad attr NameLoad decorator_list decorator_list
N/A __future__ division numpy dot zeros ones inf logical_and sort unique neupy.utils format_data neupy.core.properties ProperFractionProperty IntProperty neupy.network.base BaseNetwork N/A __all__ N/A ART1 ART1 N/A BaseNetwork N/A N/A  Adaptive Resonance Theory (ART1) Network for binary
    data clustering.

    Notes
    -----
    * Weights are not random, so the result will be always reproduceble.

    Parameters
    ----------
    rho : float
        Control reset action in training process. Value must be
        between ``0`` and ``1``, defaults to ``0.5``.
    n_clusters : int
        Number of clusters, defaults to ``2``. Min value is also ``2``.
    {BaseNetwork.step}
    {BaseNetwork.show_epoch}
    {BaseNetwork.shuffle_data}
    {BaseNetwork.epoch_end_signal}
    {BaseNetwork.train_end_signal}

    Methods
    -------
    train(input_data):
        Network network will train until it clusters all samples.
    {BaseSkeleton.predict}
    {BaseSkeleton.fit}
    {BaseNetwork.plot_errors}

    Examples
    --------
    >>> import numpy as np
    >>> from neupy import algorithms
    >>>
    >>> data = np.array([
    ...     [0, 1, 0],
    ...     [1, 0, 0],
    ...     [1, 1, 0],
    ... ])
    >>>>
    >>> artnet = algorithms.ART1(
    ...     step=2,
    ...     rho=0.7,
    ...     n_clusters=2,
    ...     verbose=False
    ... )
    >>> artnet.predict(data)
    array([ 0.,  1.,  1.])
     N/A rho N/A ProperFractionProperty default 0.5 N/A n_clusters N/A IntProperty default 2 minval 2 train N/A N/A self input_data N/A N/A N/A input_data N/A format_data input_data N/A N/A N/A input_data ndim 2 N/A N/A N/A ValueError N/A N/A Input value must be 2 dimentional, got {0} format N/A input_data ndim N/A data_size N/A N/A input_data shape N/A 1 N/A n_clusters N/A self n_clusters N/A step N/A self step N/A rho N/A self rho N/A N/A N/A list N/A sort N/A unique input_data N/A 0 1 N/A N/A N/A ValueError ART1 Network works only with binary matrix, all matix must contains only 0 and 1 N/A N/A N/A hasattr self weight_21 N/A N/A N/A self weight_21 N/A ones N/A data_size n_clusters N/A N/A N/A hasattr self weight_12 N/A N/A N/A self weight_12 N/A N/A step N/A N/A step n_clusters 1 N/A N/A self weight_21 T N/A weight_21 N/A self weight_21 N/A weight_12 N/A self weight_12 N/A N/A data_size N/A N/A weight_21 shape N/A 0 N/A N/A N/A ValueError N/A N/A Data dimention is invalid. Get {} columns data set. Must be - {} columns format data_size N/A N/A weight_21 shape N/A 0 N/A classes N/A zeros N/A N/A input_data shape N/A 0 N/A N/A i p N/A enumerate input_data N/A N/A disabled_neurons N/A N/A reseted_values N/A N/A reset True N/A reset N/A N/A output1 p N/A input2 N/A dot weight_12 N/A output1 T N/A output2 N/A zeros N/A input2 size N/A N/A input2 N/A disabled_neurons N/A inf N/A winner_index N/A N/A input2 argmax N/A N/A output2 N/A winner_index 1 N/A expectation N/A dot weight_21 output2 N/A output1 N/A N/A N/A logical_and p expectation astype int N/A reset_value N/A N/A dot N/A output1 T output1 N/A dot N/A p T p N/A reset N/A reset_value rho N/A reset N/A N/A N/A N/A disabled_neurons append winner_index N/A N/A N/A reseted_values append N/A reset_value winner_index N/A N/A N/A len disabled_neurons n_clusters N/A N/A reset False N/A winner_index None N/A N/A reset N/A N/A N/A winner_index None N/A N/A N/A weight_12 N/A N/A winner_index N/A N/A N/A step output1 N/A N/A step N/A dot N/A output1 T output1 1 N/A N/A weight_21 N/A N/A N/A winner_index output1 N/A N/A winner_index N/A N/A max reseted_values N/A 1 N/A N/A classes N/A i winner_index N/A classes N/A predict N/A N/A self input_data N/A N/A N/A N/A N/A self train input_data N/A N/A