Module Expr Str Import alias identifier ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias ImportFrom alias alias ImportFrom alias ImportFrom alias ImportFrom alias ClassDef bases NameLoad body Expr Str FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Str NameLoad Num NameLoad Str NameLoad Num body Assert Call NameLoad NameLoad NameLoad Str Assert Call NameLoad NameLoad NameLoad Str Assert CompareGt NameLoad Num Str Assign AttributeStore NameLoad attr Call NameLoad NameLoad Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr If CompareIn NameLoad TupleLoad AttributeLoad NameLoad attr AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr NameLoad orelse If BoolOpOr CompareIn Str Call AttributeLoad NameLoad attr CompareIn Str NameLoad body Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr orelse If BoolOpOr CompareIn Str NameLoad CompareIn Str NameLoad body Assign AttributeStore NameLoad attr AttributeLoad NameLoad attr orelse Print BinOpMod Str NameLoad Assign AttributeStore NameLoad attr NameLoad If CompareIs NameLoad Str body Print Str ImportFrom alias Assign AttributeStore NameLoad attr Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad orelse If CompareIs NameLoad Str body Print Str Assign AttributeStore NameLoad attr Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad orelse Assign AttributeStore NameLoad attr Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr keyword NameLoad Assign AttributeStore NameLoad attr NameLoad If CompareIn Call AttributeLoad NameLoad attr TupleLoad Str Str Str Str body Assign AttributeStore NameLoad attr Call AttributeLoad NameLoad attr Str Str Assign AttributeStore NameLoad attr NameLoad If CompareIsNot NameLoad NameLoad body Assign NameStore Call AttributeLoad NameLoad attr NameLoad Assert CompareEq Call NameLoad NameLoad NameLoad Str Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr ListLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr TupleLoad Str Str Str Str Str Str decorator_list FunctionDef arguments args NameParam defaults body Assign NameStore BinOpMod Str TupleLoad AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad AttributeLoad NameLoad attr attr AugAssignAdd NameStore Str For TupleStore NameStore NameStore NameStore NameStore AttributeLoad AttributeLoad NameLoad attr attr body AugAssignAdd NameStore BinOpMod Str TupleLoad NameLoad NameLoad Assign NameStore SubscriptLoad NameLoad Slice Num Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assert CompareGt Call NameLoad AttributeLoad AttributeLoad NameLoad attr attr Num Str Assign NameStore ListComp Call AttributeLoad NameLoad attr comprehension NameStore NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assign AttributeStore NameLoad attr NameLoad Assert CompareLtE Call NameLoad Call AttributeLoad Call NameLoad NameLoad attr Set Str Str Str Num Str Assign NameStore Str Assert CompareLtE Call NameLoad Call AttributeLoad Call NameLoad NameLoad attr Set Str Str Str Str Num NameLoad For NameStore NameLoad body If CompareEq NameLoad Str body Assign AttributeStore NameLoad attr Str If CompareIn Str Call AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr Call NameLoad SubscriptLoad NameLoad Index Str If CompareEq NameLoad Str body Assert CompareGt AttributeLoad AttributeLoad NameLoad attr attr Num Str Assign AttributeStore NameLoad attr Str If CompareEq NameLoad Str body Assert CompareGt AttributeLoad AttributeLoad NameLoad attr attr Num Str Assign AttributeStore NameLoad attr Str If CompareIn Str Call AttributeLoad NameLoad attr body Assign NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index Str Assert CompareEq Call NameLoad NameLoad AttributeLoad AttributeLoad NameLoad attr attr Str Assign AttributeStore NameLoad attr NameLoad If BoolOpOr CompareEq NameLoad Str CompareEq NameLoad Str body Assert CompareGt AttributeLoad AttributeLoad NameLoad attr attr Num Str Assign AttributeStore NameLoad attr Str If CompareEq NameLoad Str body Assign AttributeStore NameLoad attr NameLoad If CompareIn Str Call AttributeLoad NameLoad attr body Assign AttributeStore NameLoad attr Call NameLoad SubscriptLoad NameLoad Index Str decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults vararg kwarg body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Expr Call AttributeLoad NameLoad attr NameLoad NameLoad If CompareIn Str NameLoad body Assert CompareIn Str Call AttributeLoad NameLoad attr Str Assert CompareIn Str Call AttributeLoad NameLoad attr Str Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore SubscriptLoad NameLoad Index Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Expr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad orelse If CompareIn Str NameLoad body Assert CompareIn Str Call AttributeLoad NameLoad attr Str Assign NameStore SubscriptLoad NameLoad Index Str Assert CompareGtE NameLoad Num Str Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad Return NameLoad orelse If CompareIn Str NameLoad body Expr Call NameLoad NameLoad NameLoad NameLoad orelse Expr Call AttributeLoad NameLoad attr NameLoad NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign NameStore Call NameLoad Call AttributeLoad NameLoad attr BinOpDiv Call NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num AttributeLoad NameLoad attr Assign NameStore NameLoad If BoolOpAnd CompareEq AttributeLoad NameLoad attr Str CompareIs AttributeLoad NameLoad attr NameLoad body Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr keyword Num attr AttributeLoad NameLoad attr Assign AttributeStore NameLoad attr BinOpDiv Call AttributeLoad NameLoad attr NameLoad For TupleStore NameStore NameStore Call NameLoad Call AttributeLoad NameLoad attr NameLoad NameLoad keyword Num Call AttributeLoad NameLoad attr NameLoad NameLoad keyword Num body If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index SubscriptLoad Call AttributeLoad NameLoad attr CompareEq NameLoad Num Index Num Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str Assert Call NameLoad NameLoad NameLoad Str Assert BoolOpOr CompareIn NameLoad AttributeLoad NameLoad attr Call NameLoad NameLoad AttributeLoad NameLoad attr BinOpMod Str NameLoad Assert Call NameLoad NameLoad TupleLoad AttributeLoad NameLoad attr Call NameLoad NameLoad Str Assert Call NameLoad NameLoad TupleLoad AttributeLoad NameLoad attr Call NameLoad NameLoad Str Assign NameStore AttributeLoad AttributeLoad NameLoad attr attr If CompareIs NameLoad NameLoad body If CompareEq NameLoad Str body Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad orelse Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad NameLoad If CompareNotIn NameLoad TupleLoad Str Str Str body AugAssignMult NameStore BinOpDiv Num BinOpPow NameLoad Num If CompareIs NameLoad NameLoad body Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad If CompareIn NameLoad TupleLoad Str Str Str body Assign NameStore Call AttributeLoad NameLoad attr NameLoad AugAssignMult NameStore NameLoad If CompareEq NameLoad Str body Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad Assign NameStore BinOpMod Str TupleLoad NameLoad NameLoad Assert CompareEq AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad NameLoad Assert CompareEq AttributeLoad NameLoad attr TupleLoad NameLoad BinOpMod Str NameLoad Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Return Call AttributeLoad NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str Assert CompareIn AttributeLoad NameLoad attr TupleLoad Str Str Str Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call NameLoad Call AttributeLoad NameLoad attr BinOpDiv Call NameLoad NameLoad AttributeLoad NameLoad attr Assign NameStore AttributeLoad AttributeLoad NameLoad attr attr Assign NameStore Call AttributeLoad NameLoad attr TupleLoad NameLoad NameLoad If CompareIn AttributeLoad NameLoad attr TupleLoad Str Str body For NameStore Call NameLoad NameLoad body Assign NameStore BinOpMult NameLoad AttributeLoad NameLoad attr Assign NameStore Call NameLoad BinOpMult BinOpAdd NameLoad Num AttributeLoad NameLoad attr NameLoad Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Slice NameLoad NameLoad attr Num Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Slice NameLoad NameLoad attr Num For NameStore Call NameLoad NameLoad body For NameStore Call NameLoad NameLoad body AugAssignAdd SubscriptStore NameLoad Index TupleLoad NameLoad NameLoad Call AttributeLoad Call AttributeLoad NameLoad attr CompareEq NameLoad NameLoad CompareEq NameLoad NameLoad attr orelse If CompareEq AttributeLoad NameLoad attr Str body For NameStore Call NameLoad NameLoad body Assign NameStore BinOpMult NameLoad AttributeLoad NameLoad attr Assign NameStore Call NameLoad BinOpMult BinOpAdd NameLoad Num AttributeLoad NameLoad attr NameLoad Assign NameStore CompareGt Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Slice NameLoad NameLoad Num Assign NameStore CompareGt Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Slice NameLoad NameLoad Num For NameStore Call NameLoad NameLoad body For NameStore Call NameLoad NameLoad body AugAssignAdd SubscriptStore NameLoad Index TupleLoad NameLoad NameLoad Call AttributeLoad NameLoad attr BinOpMult SubscriptLoad NameLoad ExtSlice Slice Index NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad NameLoad attr NameLoad NameLoad Assign NameStore Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assert Call NameLoad NameLoad NameLoad Str Assign NameStore Dict Str Str Str Str Str Str Str AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad NameLoad attr AttributeLoad NameLoad attr Call AttributeLoad AttributeLoad NameLoad attr attr AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad AttributeLoad NameLoad attr attr TryExcept body Expr Call AttributeLoad NameLoad attr NameLoad Call NameLoad NameLoad Str Num handlers ExceptHandler type NameLoad body Raise Call NameLoad BinOpMod Str NameLoad decorator_list FunctionDef arguments args NameParam NameParam defaults body Expr Str Assert Call NameLoad NameLoad NameLoad Str TryExcept body Assign NameStore Call AttributeLoad NameLoad attr Call NameLoad NameLoad Str handlers ExceptHandler type NameLoad body Raise Call NameLoad BinOpMod Str NameLoad Assign NameStore SubscriptLoad NameLoad Index Str Assign NameStore SubscriptLoad NameLoad Index Str Assign AttributeStore NameLoad attr SubscriptLoad NameLoad Index Str Assign AttributeStore NameLoad attr SubscriptLoad NameLoad Index Str If CompareIs AttributeLoad NameLoad attr NameLoad body Assign AttributeStore NameLoad attr Call NameLoad NameLoad NameLoad keyword AttributeLoad NameLoad attr For TupleStore NameStore NameStore NameStore NameStore SubscriptLoad NameLoad Index Str body Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad NameLoad Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr Assign AttributeStore AttributeLoad NameLoad attr attr SubscriptLoad NameLoad Index Str If CompareIsNot SubscriptLoad NameLoad Index Str NameLoad body Expr Call AttributeLoad AttributeLoad NameLoad attr attr Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index Str keyword AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam defaults body For NameStore AttributeLoad NameLoad attr body Expr Call AttributeLoad NameLoad attr decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad body Expr Str If CompareIs NameLoad NameLoad body If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr Num Call AttributeLoad NameLoad attr Num attr orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad For NameStore Call NameLoad NameLoad body Assign NameStore SubscriptLoad Call AttributeLoad NameLoad attr CompareEq SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Num Index Num If CompareGt Call NameLoad NameLoad Num body Assign SubscriptStore NameLoad Index NameLoad Call AttributeLoad Call AttributeLoad NameLoad attr Call AttributeLoad SubscriptLoad NameLoad Index NameLoad attr Num NameLoad attr Assign NameStore BinOpDiv Call AttributeLoad NameLoad attr BinOpMult NameLoad AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr AttributeLoad NameLoad attr orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore Call AttributeLoad Call AttributeLoad NameLoad attr CompareGt NameLoad Num CompareGt NameLoad Num attr orelse Assign NameStore Call AttributeLoad NameLoad attr BinOpPow BinOpSub NameLoad NameLoad Num orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore BinOpDiv Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr Num Call AttributeLoad NameLoad attr Num Call AttributeLoad NameLoad attr Assign NameStore Call AttributeLoad NameLoad attr BinOpPow NameLoad Num orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore SubscriptLoad AttributeLoad NameLoad attr Index Num Assign NameStore Call AttributeLoad NameLoad attr NameLoad For NameStore Call NameLoad NameLoad body Assign NameStore SubscriptLoad Call AttributeLoad NameLoad attr CompareEq SubscriptLoad NameLoad ExtSlice Slice Index NameLoad Num Index Num If CompareGt Call NameLoad NameLoad Num body Assign NameStore BinOpDiv Call AttributeLoad NameLoad attr Call AttributeLoad SubscriptLoad NameLoad Index NameLoad attr Num NameLoad Call AttributeLoad SubscriptLoad NameLoad Index NameLoad attr Assign SubscriptStore NameLoad Index NameLoad Call AttributeLoad NameLoad attr BinOpPow NameLoad Num Assign NameStore Call AttributeLoad NameLoad attr BinOpMult NameLoad AttributeLoad NameLoad attr orelse If CompareEq AttributeLoad NameLoad attr Str body Assign NameStore BinOpDiv Call AttributeLoad NameLoad attr CompareGt NameLoad Num CompareGt NameLoad Num Call AttributeLoad NameLoad attr TupleLoad Num Num Assign NameStore Call AttributeLoad NameLoad attr BinOpPow NameLoad Num orelse Assign NameStore BinOpDiv BinOpSub NameLoad NameLoad Call AttributeLoad NameLoad attr TupleLoad Num Num Assign NameStore Call AttributeLoad NameLoad attr BinOpPow NameLoad Num Assert UnaryOpNot Call AttributeLoad NameLoad attr NameLoad BinOpMod Str AttributeLoad NameLoad attr Return Call AttributeLoad NameLoad attr NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam defaults NameLoad NameLoad body Expr Str If CompareEq AttributeLoad NameLoad attr Str body Assert BoolOpAnd CompareIsNot NameLoad NameLoad CompareIsNot NameLoad NameLoad Str If CompareIsNot AttributeLoad NameLoad attr NameLoad body Assign NameStore Call NameLoad AttributeLoad NameLoad attr NameLoad If CompareLt SubscriptLoad AttributeLoad NameLoad attr Index Num Num body Assign NameStore Call NameLoad NameLoad NameLoad NameLoad orelse Assign NameStore Call NameLoad NameLoad NameLoad NameLoad orelse Assign NameStore Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Return TupleLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam defaults body Expr Str If CompareIsNot NameLoad NameLoad body If Call NameLoad NameLoad NameLoad body TryExcept body Assign NameStore Call NameLoad NameLoad Str handlers ExceptHandler body Raise Call NameLoad BinOpMod Str NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Assign NameStore NameLoad For NameStore Call AttributeLoad NameLoad attr body Pass If NameLoad body Assign NameStore NameLoad orelse Raise Call NameLoad BinOpMod Str NameLoad orelse Assert CompareNotIn AttributeLoad AttributeLoad NameLoad attr attr Str Str If CompareEq Call NameLoad AttributeLoad NameLoad attr Num body Assign NameStore Call AttributeLoad NameLoad attr Num Num Assert CompareEq Call NameLoad AttributeLoad NameLoad attr Num Str Assert CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num AttributeLoad AttributeLoad NameLoad attr attr BinOpMod Str TupleLoad AttributeLoad AttributeLoad NameLoad attr attr SubscriptLoad AttributeLoad NameLoad attr Index Num If CompareIsNot NameLoad NameLoad body If Call NameLoad NameLoad NameLoad body TryExcept body Assign NameStore Call NameLoad NameLoad Str handlers ExceptHandler type NameLoad body Raise Call NameLoad BinOpMod Str NameLoad Expr Call AttributeLoad AttributeLoad NameLoad attr attr NameLoad Assign NameStore NameLoad For NameStore Call AttributeLoad NameLoad attr body Pass If NameLoad body Assign NameStore NameLoad orelse Raise Call NameLoad BinOpMod Str NameLoad orelse Assert CompareNotIn AttributeLoad AttributeLoad NameLoad attr attr Str Str If CompareEq Call NameLoad AttributeLoad NameLoad attr Num body Assign NameStore Call AttributeLoad NameLoad attr Num Num Assert CompareEq Call NameLoad AttributeLoad NameLoad attr Num Str Assert CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num AttributeLoad AttributeLoad NameLoad attr attr BinOpMod Str TupleLoad AttributeLoad AttributeLoad NameLoad attr attr SubscriptLoad AttributeLoad NameLoad attr Index Num If BoolOpAnd CompareIsNot NameLoad NameLoad CompareIsNot NameLoad NameLoad body Assert CompareEq SubscriptLoad AttributeLoad NameLoad attr Index Num SubscriptLoad AttributeLoad NameLoad attr Index Num Str Return TupleLoad NameLoad NameLoad decorator_list decorator_list
N/A N/A 
Created on Mon Oct 27 17:48:33 2014

@author: akusok
 N/A numpy np six.moves cPickle six integer_types tables open_file nnets.slfn SLFN nnets.slfn_python SLFNPython modules mrsr mrsr2 mss_v train_v mss_cv train_cv mss_loo train_loo ELM N/A object N/A N/A Interface for training Extreme Learning Machines (ELM).

    Args:
        inputs (int): dimensionality of input data, or number of data features
        outputs (int): dimensionality of output data, or number of classes
        classification ('c'/'wc'/'ml', optional): train ELM for classfication ('c') / weighted classification ('wc') /
            multi-label classification ('ml'). For weighted classification you can provide weights in `w`. ELM will
            compute and use the corresponding classification error instead of Mean Squared Error.
        w (vector, optional): weights vector for weighted classification, lenght (`outputs` * 1).
        batch (int, optional): batch size for data processing in ELM, reduces memory requirements. Does not work
            for model structure selection (validation, cross-validation, Leave-One-Out). Can be changed later directly
            as a class attribute.
        accelerator ("GPU"/"basic", optional): type of accelerated ELM to use: None, 'GPU', 'basic', ...
        precision (optional): data precision to use, supports signle ('single', '32' or numpy.float32) or double
            ('double', '64' or numpy.float64). Single precision is faster but may cause numerical errors. Majority
            of GPUs work in single precision. Default: **double**.
        norm (double, optinal): L2-normalization parameter, **None** gives the default value.
        tprint (int, optional): ELM reports its progess every `tprint` seconds or after every batch,
            whatever takes longer.

    Class attributes; attributes that simply store initialization or `train()` parameters are omitted.

    Attributes:
        nnet (object): Implementation of neural network with computational methods, but without
            complex logic. Different implementations are given by different classes: for Python, for GPU, etc.
            See ``hpelm.nnets`` folder for particular files. You can implement your own computational algorithm
            by inheriting from ``hpelm.nnets.SLFN`` and overwriting some methods.
        flist (list of strings): Awailable types of neurons, use them when adding new neurons.

    Note:
        Below the 'matrix' type means a 2-dimensional Numpy.ndarray.
     __init__ N/A N/A self inputs outputs classification w batch accelerator precision norm tprint N/A None 1000 None double None 5 N/A N/A N/A isinstance inputs integer_types Number of inputs must be integer N/A N/A isinstance outputs integer_types Number of outputs must be integer N/A N/A batch 0 Batch should be positive N/A N/A self batch N/A int batch N/A N/A self precision N/A np float64 N/A N/A precision N/A N/A np float32 N/A np float64 N/A N/A N/A self precision precision N/A N/A N/A N/A double N/A N/A precision lower N/A 64 precision N/A N/A N/A self precision N/A np float64 N/A N/A N/A N/A single precision N/A 32 precision N/A N/A N/A self precision N/A np float32 N/A N/A N/A Unknown precision parameter: %s, using double precision precision N/A N/A self accelerator accelerator N/A N/A accelerator GPU N/A N/A Using CUDA GPU acceleration with Scikit-CUDA nnets.slfn_skcuda SLFNSkCUDA N/A N/A self nnet N/A SLFNSkCUDA inputs outputs precision N/A self precision norm norm N/A N/A N/A accelerator basic N/A N/A Using slower basic Python solver N/A N/A self nnet N/A SLFN inputs outputs precision N/A self precision norm norm N/A N/A N/A self nnet N/A SLFNPython inputs outputs precision N/A self precision norm norm N/A N/A self classification None N/A N/A N/A N/A classification lower N/A c wc ml mc N/A N/A N/A self classification N/A N/A classification replace mc ml N/A N/A self wc None N/A N/A w None N/A N/A w N/A N/A np array w N/A N/A N/A len w outputs Number of class weights must be equal to the number of classes N/A N/A self wc w N/A N/A self opened_hdf5 N/A N/A N/A self ranking None N/A N/A self kmax_op None N/A N/A self tprint tprint N/A N/A self flist N/A lin sigm tanh rbf_l1 rbf_l2 rbf_linf N/A __str__ N/A N/A self N/A N/A N/A s N/A ELM with %d inputs and %d outputs
 N/A N/A N/A self nnet inputs N/A N/A self nnet outputs N/A s Hidden layer neurons:  N/A N/A n func _ _ N/A N/A self nnet neurons N/A N/A s N/A %d %s,  N/A n func N/A s N/A s N/A -2 N/A s N/A _train_parse_args N/A N/A self args kwargs N/A N/A N/A Parse training args and set corresponding class variables. N/A N/A N/A len N/A N/A self nnet neurons 0 Add neurons to ELM before training it N/A args N/A N/A N/A a upper N/A a args N/A N/A N/A N/A self nnet reset N/A N/A self ranking None N/A N/A self kmax_op None N/A N/A self classification None N/A N/A self wc None N/A N/A N/A len N/A N/A N/A set args intersection N/A V CV LOO 1 Use only one of V / CV / LOO N/A msg Use only one of: C (classification) / WC (weighted classification) / ML (multi-label classification) N/A N/A N/A len N/A N/A N/A set args intersection N/A C WC ML MC 1 msg N/A a args N/A N/A N/A a OP N/A N/A N/A self ranking OP N/A N/A kmax N/A N/A kwargs keys N/A N/A N/A self kmax_op N/A int N/A kwargs N/A kmax N/A N/A a C N/A N/A N/A N/A N/A self nnet outputs 1 Classification outputs must have 1 output per class N/A N/A self classification c N/A N/A a WC N/A N/A N/A N/A N/A self nnet outputs 1 Classification outputs must have 1 output per class N/A N/A self classification wc N/A N/A w N/A N/A kwargs keys N/A N/A w N/A N/A np array N/A kwargs N/A w N/A N/A N/A len w N/A N/A self nnet outputs Number of class weights must be equal to the number of classes N/A N/A self wc w N/A N/A N/A a ML N/A a MC N/A N/A N/A N/A N/A self nnet outputs 1 Classification outputs must have 1 output per class N/A N/A self classification ml N/A N/A a R N/A N/A N/A self classification None N/A N/A batch N/A N/A kwargs keys N/A N/A N/A self batch N/A int N/A kwargs N/A batch N/A train N/A N/A self X T N/A args kwargs N/A N/A Universal training interface for ELM model with model structure selection.

        Model structure selection takes more time and requires all data to fit into memory. Optimal pruning ('OP',
        effectively an L1-regularization) takes the most time but gives the smallest and best performing model.
        Choosing a classification forces ELM to use classification error in model structure selection,
        and in `error()` method output.

        Args:
            X (matrix): input data matrix, size (N * `inputs`)
            T (matrix): outputs data matrix, size (N * `outputs`)
            'V'/'CV'/'LOO' (sting, choose one): model structure selection: select optimal number of neurons using
                a validation set ('V'), cross-validation ('CV') or Leave-One-Out ('LOO')
            'OP' (string, use with 'V'/'CV'/'LOO'): choose best neurons instead of random ones, training takes longer;
                equivalent to L1-regularization
            'c'/'wc'/'ml'/'r' (string, choose one): train ELM for classification ('c'), classification with weighted
                classes ('wc'), multi-label classification ('ml') with several correct classes per data sample, or
                regression ('r') without any classification. In classification, number of `outputs` is the number
                of classes; correct class(es) for each sample has value 1 and incorrect classes have 0.
                Overwrites parameters given an ELM initialization time.

        Keyword Args:
            Xv (matrix, use with 'V'): validation set input data, size (Nv * `inputs`)
            Tv (matrix, use with 'V'): validation set outputs data, size (Nv * `outputs`)
            k (int, use with 'CV'): number of splits for cross-validation, k>=3
            kmax (int, optional, use with 'OP'): maximum number of neurons to keep in ELM
            batch (int, optional): batch size for ELM, overwrites batch size from the initialization

        Returns:
            e (double, for 'CV'): test error for cross-validation, computed from one separate test chunk in each
                split of data during the cross-validation procedure
         N/A N/A X T N/A N/A self _checkdata X T N/A N/A N/A self _train_parse_args args kwargs N/A N/A V args N/A N/A N/A Xv N/A N/A kwargs keys Provide validation dataset (Xv) N/A N/A Tv N/A N/A kwargs keys Provide validation outputs (Tv) N/A Xv N/A kwargs N/A Xv N/A Tv N/A kwargs N/A Tv N/A N/A Xv Tv N/A N/A self _checkdata Xv Tv N/A N/A train_v self X T Xv Tv N/A N/A N/A CV args N/A N/A N/A k N/A N/A kwargs keys Provide Cross-Validation number of splits (k) N/A k N/A kwargs N/A k N/A N/A k 3 Use at least k=3 splits for Cross-Validation N/A e N/A train_cv self X T k N/A e N/A N/A N/A LOO args N/A N/A N/A train_loo self X T N/A N/A N/A N/A self add_data X T N/A N/A N/A N/A self nnet solve N/A add_data N/A N/A self X T N/A N/A N/A Feed new training data (X,T) to ELM model in batches; does not solve ELM itself.

        Helper method that updates intermediate solution parameters HH and HT, which are used for solving ELM later.
        Updates accumulate, so this method can be called multiple times with different parts of training data.
        To reset accumulated training data, use `ELM.nnet.reset()`.

        For training an ELM use `ELM.train()` instead.

        Args:
            X (matrix): input training data
            T (matrix): output training data
         N/A nb N/A int N/A N/A np ceil N/A N/A float N/A N/A X shape N/A 0 N/A self batch N/A wc_vector None N/A N/A N/A N/A self classification wc N/A N/A self wc None N/A N/A ns N/A N/A N/A N/A T sum axis 0 astype N/A self precision N/A N/A self wc N/A N/A N/A ns sum ns N/A N/A X0 T0 N/A zip N/A N/A np array_split X nb axis 0 N/A N/A np array_split T nb axis 0 N/A N/A N/A N/A self classification wc N/A N/A wc_vector N/A N/A self wc N/A N/A N/A N/A np where N/A T0 1 N/A 1 N/A N/A N/A N/A self nnet add_batch X0 T0 wc_vector N/A add_neurons N/A N/A self number func W B N/A None None N/A N/A Adds neurons to ELM model. ELM is created empty, and needs some neurons to work.

        Add neurons to an empty ELM model, or add more neurons to a model that already has some.

        Random weights `W` and biases `B` are generated automatically if not provided explicitly.
        Maximum number of neurons is limited by the available RAM and computational power, a sensible limit
        would be 1000 neurons for an average size dataset and 15000 for the largest datasets. ELM becomes slower after
        3000 neurons because computational complexity is proportional to a qube of number of neurons.

        This method checks and prepares neurons, they are actually stored in `solver` object.

        Args:
            number (int): number of neurons to add
            func (string): type of neurons: "lin" for linear, "sigm" or "tanh" for non-linear,
                "rbf_l1", "rbf_l2" or "rbf_linf" for radial basis function neurons.
            W (matrix, optional): random projection matrix size (`inputs` * `number`). For 'rbf_' neurons,
                W stores centroids of radial basis functions in transposed form.
            B (vector, optional): bias vector of size (`number` * 1), a 1-dimensional Numpy.ndarray object.
                For 'rbf_' neurons, B gives widths of radial basis functions.
         N/A N/A isinstance number integer_types Number of neurons must be integer N/A N/A N/A func N/A self flist N/A isinstance func N/A np ufunc N/A '%s' neurons not suppored: use a standard neuron function or a custom <numpy.ufunc> func N/A N/A isinstance W N/A N/A np ndarray N/A type None Projection matrix (W) must be a Numpy ndarray N/A N/A isinstance B N/A N/A np ndarray N/A type None Bias vector (B) must be a Numpy ndarray N/A inputs N/A N/A self nnet inputs N/A N/A W None N/A N/A N/A func lin N/A N/A number N/A min number inputs N/A W N/A N/A np eye inputs number N/A N/A W N/A N/A N/A np random randn inputs number N/A N/A func N/A rbf_l1 rbf_l2 rbf_linf N/A N/A W N/A 3.0 N/A inputs 0.5 N/A N/A B None N/A N/A B N/A N/A N/A np random randn number N/A N/A func N/A rbf_l2 rbf_l1 rbf_linf N/A N/A B N/A N/A np abs B N/A B inputs N/A N/A func lin N/A N/A B N/A N/A np zeros N/A number N/A msg N/A W must be size [inputs, neurons] (expected [%d,%d]) N/A inputs number N/A N/A N/A W shape N/A inputs number msg N/A N/A N/A B shape N/A number N/A B must be size [neurons] (expected [%d]) number N/A W N/A N/A W astype N/A self precision N/A B N/A N/A B astype N/A self precision N/A N/A N/A N/A self nnet add_neurons number func W B N/A error N/A N/A self T Y N/A N/A N/A Calculate error of model predictions.

        Computes Mean Squared Error (MSE) between model predictions Y and true outputs T.
        For classification, computes mis-classification error.
        For multi-label classification, correct classes are all with Y>0.5.

        For weighted classification the error is an average weighted True Positive Rate,
        or percentage of correctly predicted samples for each class, multiplied by weight
        of that class and averaged. If you want something else, just write it yourself :)
        See https://en.wikipedia.org/wiki/Confusion_matrix for details.

        Another option is to use scikit-learn's performance metrics. Transform `Y` and `T` into scikit's
        format by ``y_true = T.argmax[1]``, ``y_pred = Y.argmax[1]``.
        http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics

        Args:
            T (matrix): true outputs.
            Y (matrix): ELM model predictions, can be computed with `predict()` function.

        Returns:
            e (double): MSE for regression / classification error for classification.
         N/A N/A _ T N/A N/A self _checkdata None T N/A N/A _ Y N/A N/A self _checkdata None Y N/A N/A N/A self _error T Y N/A confusion N/A N/A self T Y N/A N/A N/A Computes confusion matrix for classification.

        Confusion matrix :math:`C` such that element :math:`C_{i,j}` equals to the number of observations known
        to be class :math:`i` but predicted to be class :math:`j`.

        Args:
            T (matrix): true outputs or classes, size (N * `outputs`)
            Y (matrix): predicted outputs by ELM model, size (N * `outputs`)

        Returns:
            conf (matrix): confusion matrix, size (`outputs` * `outputs`)
         N/A N/A N/A self classification N/A c wc ml Confusion matrix works only for regression N/A N/A _ T N/A N/A self _checkdata None T N/A N/A _ Y N/A N/A self _checkdata None Y N/A N N/A N/A T shape N/A 0 N/A nb N/A int N/A N/A np ceil N/A N/A float N N/A self batch N/A C N/A N/A self nnet outputs N/A conf N/A N/A np zeros N/A C C N/A N/A N/A self classification N/A c wc N/A N/A b N/A xrange nb N/A N/A start N/A b N/A self batch N/A stop N/A min N/A N/A b 1 N/A self batch N N/A Tb N/A N/A N/A N/A np array N/A T N/A start stop argmax 1 N/A Yb N/A N/A N/A N/A np array N/A Y N/A start stop argmax 1 N/A c1 N/A xrange C N/A N/A c1h N/A xrange C N/A N/A N/A conf N/A N/A c1 c1h N/A N/A N/A N/A np logical_and N/A Tb c1 N/A Yb c1h sum N/A N/A N/A N/A self classification ml N/A N/A b N/A xrange nb N/A N/A start N/A b N/A self batch N/A stop N/A min N/A N/A b 1 N/A self batch N N/A Tb N/A N/A N/A np array N/A T N/A start stop 0.5 N/A Yb N/A N/A N/A np array N/A Y N/A start stop 0.5 N/A c1 N/A xrange C N/A N/A c1h N/A xrange C N/A N/A N/A conf N/A N/A c1 c1h N/A N/A np sum N/A N/A Tb N/A N/A N/A c1 N/A Yb N/A N/A N/A c1h N/A conf N/A project N/A N/A self X N/A N/A N/A Get ELM's hidden layer representation of input data.

        Args:
            X (matrix): input data, size (N * `inputs`)

        Returns:
            H (matrix): hidden layer representation matrix, size (N * number_of_neurons)
         N/A N/A X _ N/A N/A self _checkdata X None N/A H N/A N/A N/A self nnet _project X N/A H N/A predict N/A N/A self X N/A N/A N/A Predict outputs Y for the given input data X.

        Args:
            X (matrix): input data of size (N * `inputs`)

        Returns:
            Y (matrix): output data or predicted classes, size (N * `outputs`).
         N/A N/A X _ N/A N/A self _checkdata X None N/A Y N/A N/A N/A self nnet _predict X N/A Y N/A save N/A N/A self fname N/A N/A N/A Save ELM model with current parameters.

        Model does not save a particular solver, precision batch size. They are obtained from
        a new ELM when loading the model (so one can switch to another solver, for instance).

        Also ranking and max number of neurons are not saved, because they
        are runtime training info irrelevant after the training completes.

        Args:
            fname (string): filename to save model into.
         N/A N/A isinstance fname basestring Model file name must be a string N/A m N/A inputs outputs Classification Weights_WC neurons norm Beta N/A N/A self nnet inputs N/A N/A self nnet outputs N/A self classification N/A self wc N/A N/A N/A self nnet get_neurons N/A N/A self nnet norm N/A N/A N/A self nnet get_B N/A N/A N/A N/A N/A cPickle dump m N/A open fname wb -1 N/A N/A N/A IOError N/A N/A N/A IOError N/A Cannot create a model file at: %s fname N/A load N/A N/A self fname N/A N/A N/A Load ELM model data from a file.

        Load requires an ``ELM`` object, and it uses solver type, precision and batch size from that ELM object.

        Args:
            fname (string): filename to load model from.
         N/A N/A isinstance fname basestring Model file name must be a string N/A N/A N/A m N/A N/A cPickle load N/A open fname rb N/A N/A N/A IOError N/A N/A N/A IOError N/A Model file not found: %s fname N/A inputs N/A m N/A inputs N/A outputs N/A m N/A outputs N/A N/A self classification N/A m N/A Classification N/A N/A self wc N/A m N/A Weights_WC N/A N/A N/A self accelerator None N/A N/A N/A self nnet N/A SLFN inputs outputs precision N/A self precision N/A N/A number func W B N/A m N/A neurons N/A N/A N/A N/A N/A self nnet add_neurons number func N/A N/A W astype N/A self precision N/A N/A B astype N/A self precision N/A N/A N/A self nnet norm N/A m N/A norm N/A N/A N/A m N/A Beta None N/A N/A N/A N/A N/A self nnet set_B N/A N/A np array N/A m N/A Beta dtype N/A self precision N/A __del__ N/A N/A self N/A N/A N/A h5 N/A self opened_hdf5 N/A N/A N/A N/A h5 close N/A _error N/A N/A self T Y R N/A None N/A N/A Returns regression/classification/multiclass error, also for PRESS.

        An ELM-specific error with PRESS support.
         N/A N/A R None N/A N/A N/A N/A self classification c N/A N/A err N/A N/A N/A N/A np not_equal N/A N/A Y argmax 1 N/A N/A T argmax 1 mean N/A N/A N/A N/A self classification wc N/A N/A c N/A N/A T shape N/A 1 N/A errc N/A N/A np zeros c N/A i N/A xrange c N/A N/A idx N/A N/A N/A np where N/A N/A T N/A N/A N/A i 1 N/A 0 N/A N/A N/A len idx 0 N/A N/A N/A errc N/A i N/A N/A N/A N/A np not_equal N/A N/A N/A Y N/A idx argmax 1 i mean N/A err N/A N/A N/A np sum N/A errc N/A self wc N/A N/A np sum N/A self wc N/A N/A N/A N/A self classification ml N/A N/A err N/A N/A N/A N/A np not_equal N/A Y 0.5 N/A T 0.5 mean N/A N/A err N/A N/A np mean N/A N/A Y T 2 N/A N/A N/A N/A self classification c N/A N/A err N/A N/A N/A np not_equal N/A N/A Y argmax 1 N/A N/A T argmax 1 N/A N/A R ravel N/A err N/A N/A np mean N/A err 2 N/A N/A N/A N/A self classification wc N/A N/A c N/A N/A T shape N/A 1 N/A errc N/A N/A np zeros c N/A i N/A xrange c N/A N/A idx N/A N/A N/A np where N/A N/A T N/A N/A N/A i 1 N/A 0 N/A N/A N/A len idx 0 N/A N/A t N/A N/A N/A np not_equal N/A N/A N/A Y N/A idx argmax 1 i N/A N/A N/A R N/A idx ravel N/A N/A errc N/A i N/A N/A np mean N/A t 2 N/A err N/A N/A np mean N/A errc N/A self wc N/A N/A N/A N/A self classification ml N/A N/A err N/A N/A N/A np not_equal N/A Y 0.5 N/A T 0.5 N/A N/A R reshape N/A -1 1 N/A err N/A N/A np mean N/A err 2 N/A N/A err N/A N/A Y T N/A N/A R reshape N/A -1 1 N/A err N/A N/A np mean N/A err 2 N/A N/A N/A N/A np isnan err N/A Error is NaN at %s N/A self classification N/A N/A N/A np float64 err N/A _ranking N/A N/A self L H T N/A None None N/A N/A Return ranking of hidden neurons; random or OP.

        Args:
            L (int): number of neurons
            H (matrix): hidden layer representation matrix needed for optimal pruning
            T (matrix): outputs matrix needed for optimal pruning

        Returns:
            rank (vector): ranking of neurons
            L (int): number of selected neurons, can be changed by `self.kmax_op`
         N/A N/A N/A self ranking OP N/A N/A N/A N/A H None N/A T None Need H and T to perform optimal pruning N/A N/A N/A self kmax_op None N/A N/A L N/A min N/A self kmax_op L N/A N/A N/A N/A T shape N/A 1 10 N/A N/A rank N/A mrsr H T L N/A N/A rank N/A mrsr2 H T L N/A N/A rank N/A N/A np arange L N/A N/A N/A N/A np random shuffle rank N/A N/A rank L N/A _checkdata N/A N/A self X T N/A N/A N/A Checks data variables and fixes matrix dimensionality issues.
         N/A N/A X None N/A N/A N/A isinstance X basestring N/A N/A N/A N/A h5 N/A open_file X r N/A N/A N/A N/A N/A IOError N/A Cannot read HDF5 file at %s X N/A N/A N/A N/A self opened_hdf5 append h5 N/A node None N/A node N/A N/A h5 walk_nodes N/A N/A N/A node N/A N/A X node N/A N/A N/A IOError N/A Empty HDF5 file at %s X N/A N/A N/A N/A N/A X dtype kind OSU X must be a numerical numpy array N/A N/A N/A len N/A X shape 1 N/A N/A X N/A N/A X reshape -1 1 N/A N/A N/A len N/A X shape 2 X must have 2 dimensions N/A N/A N/A N/A X shape N/A 1 N/A N/A self nnet inputs N/A X has wrong dimensionality: expected %d, found %d N/A N/A N/A self nnet inputs N/A N/A X shape N/A 1 N/A N/A T None N/A N/A N/A isinstance T basestring N/A N/A N/A N/A h5 N/A open_file T r N/A N/A N/A IOError N/A N/A N/A IOError N/A Cannot read HDF5 file at %s T N/A N/A N/A N/A self opened_hdf5 append h5 N/A node None N/A node N/A N/A h5 walk_nodes N/A N/A N/A node N/A N/A T node N/A N/A N/A IOError N/A Empty HDF5 file at %s X N/A N/A N/A N/A N/A T dtype kind OSU T must be a numerical numpy array N/A N/A N/A len N/A T shape 1 N/A N/A T N/A N/A T reshape -1 1 N/A N/A N/A len N/A T shape 2 T must have 2 dimensions N/A N/A N/A N/A T shape N/A 1 N/A N/A self nnet outputs N/A T has wrong dimensionality: expected %d, found %d N/A N/A N/A self nnet outputs N/A N/A T shape N/A 1 N/A N/A N/A X None N/A T None N/A N/A N/A N/A N/A X shape N/A 0 N/A N/A T shape N/A 0 X and T cannot have different number of samples N/A N/A X T N/A N/A