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¶þÿòµþÿ—¶þÿhºþÿ“ºþÿ¾ºþÿéºþÿ»þÿ?»þÿr»þÿ¥»þÿػþÿ¼þÿ>¼þÿq¼þÿ¤¼þÿ׼þÿ½þÿ æþÿæþÿáþÿáþÿüàþÿðàþÿäàþÿÕàþÿÆàþÿ·àþÿ¨àþÿ\çþÿSçþÿJçþÿAçþÿ8çþÿ/çþÿ&çþÿçþÿçþÿçþÿöæþÿêæþÿÞæþÿÏæþÿÀæþÿ±æþÿ¢æþÿGçþÿëþÿCëþÿnëþÿ™ëþÿÄëþÿïëþÿ"ìþÿUìþÿˆìþÿ»ìþÿîìþÿ!íþÿTíþÿ‡íþÿ¶íþÿFormat string allocated too short.sample_weight and X have incompatible shapes: numpy.core.umath failed to importNumber of samples is less than number of foldsNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)numpy.core.multiarray failed to import
Binding for libsvm_skl
----------------------

These are the bindings for libsvm_skl, which is a fork of libsvm[1]
that adds to libsvm some capabilities, like index of support vectors
and efficient representation of dense matrices.

These are low-level routines, but can be used for flexibility or
performance reasons. See sklearn.svm for a higher-level API.

Low-level memory management is done in libsvm_helper.c. If we happen
to run out of memory a MemoryError will be raised. In practice this is
not very helpful since high chances are malloc fails inside svm.cpp,
where no sort of memory checks are done.

[1] https://www.csie.ntu.edu.tw/~cjlin/libsvm/

Notes
-----
Maybe we could speed it a bit further by decorating functions with
@cython.boundscheck(False), but probably it is not worth since all
work is done in lisvm_helper.c
Also, the signature mode='c' is somewhat superficial, since we already
check that arrays are C-contiguous in svm.py

Authors
-------
2010: Fabian Pedregosa <fabian.pedregosa@inria.fr>
      Gael Varoquaux <gael.varoquaux@normalesup.org>
Seems we've run out of memorysklearn/svm/_libsvm.pyxWe've run out of memorysklearn.svm._libsvm samples while X has LIBSVM_KERNEL_TYPESset_verbosity_wrapcline_in_tracebackclass_weight_labelsample_weight has decision_functioncross_validationsupport_vectorssample_weightpredict_probakernel_indexclass_weightRuntimeErrorrandom_seedprobabilityprecomputedMemoryErrorImportErrorn_class_SVfit_statuserror_repldec_valuescache_sizeValueErrorverbosityshrinkingintercepterror_msgepsilon < 0warningssvm_typemax_itersv_coefsupportsigmoidproblempredictn_classfloat64epsilontargetn_foldlinearkernel__import__degreearangeSV_lenprobBprobAparamnumpymodelint32indexgammaemptydtypecoef0array__test__polyones__name____main__tolrbfp < 0nSVfitrvnunrnpSVYXCBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectDoes not understand character buffer dtype format string ('%c')Cannot convert %.200s to %.200s%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'Argument '%.200s' has incorrect type (expected %.200s, got %.200s)__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)value too large to convert to intcalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionUnexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)sklearn.svm._libsvm.set_predict_paramsBuffer acquisition failed on assignment; and then reacquiring the old buffer failed too!sklearn.svm._libsvm.cross_validation%.200s() takes %.8s %zd positional argument%.1s (%zd given)c-string too long to convert to Pythonsklearn.svm._libsvm.decision_functionsklearn.svm._libsvm.predict_probasklearn.svm._libsvm.set_verbosity_wrapcompiletime version %s of module '%.100s' does not match runtime version %s_ARRAY_API is not PyCapsule objectmodule compiled against ABI version 0x%x but this version of numpy is 0x%xmodule compiled against API version 0x%x but this version of numpy is 0x%xFATAL: module compiled as unknown endianFATAL: module compiled as little endian, but detected different endianness at runtimeš™™™™™¹?à?ü©ñÒMbP?ð?Y@ð¿@€Exceeds max_iter in multiclass_prob
Line search fails in two-class probability estimates
Reaching maximal iterations in two-class probability estimates

Warning: using -h 0 may be faster
WARN: libsvm Solver reached max_iter
optimization finished, #iter = %d
Model doesn't contain information for SVR probability inference
Invalid input - all samples with positive weights have the same label.Invalid input - all samples have zero or negative weights.degree of polynomial kernel < 0shrinking != 0 and shrinking != 1probability != 0 and probability != 1one-class SVM probability output not supported yetProb. model for test data: target value = predicted value + z,
z: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma= %g
warning: class label %d specified in weight is not found

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ÿÿ(ÿÿˆ+ÿÿèÿÿÀÿÿ˜"ÿÿ´IÿÿÔIÿÿôIÿÿœIÿÿÔHÿÿpJÿÿ€JÿÿØJÿÿ(JÿÿXJÿÿôgÿÿlkÿÿÌYÿÿ¤]ÿÿ|bÿÿ.*C = %f
epsilon = %f
obj = %f, rho = %f
nSV = %d, nBSV = %d
unknown svm typeunknown kernel typegamma < 0cache_size <= 0eps <= 0C <= 0p < 0specified nu is infeasiblenu <= 0 or nu > 1Total nSV = %d
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    Control verbosity of libsvm library
    
    Binding of the cross-validation routine (low-level routine)

    Parameters
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    X : array-like, dtype=float of shape (n_samples, n_features)

    Y : array, dtype=float of shape (n_samples,)
        target vector

    n_fold : int32
        Number of folds for cross validation.

    svm_type : {0, 1, 2, 3, 4}, default=0
        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
        respectively.

    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default='rbf'
        Kernel to use in the model: linear, polynomial, RBF, sigmoid
        or precomputed.

    degree : int32, default=3
        Degree of the polynomial kernel (only relevant if kernel is
        set to polynomial).

    gamma : float64, default=0.1
        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
        kernels.

    coef0 : float64, default=0.0
        Independent parameter in poly/sigmoid kernel.

    tol : float64, default=1e-3
        Numeric stopping criterion (WRITEME).

    C : float64, default=1
        C parameter in C-Support Vector Classification.

    nu : float64, default=0.5
        An upper bound on the fraction of training errors and a lower bound of
        the fraction of support vectors. Should be in the interval (0, 1].

    epsilon : double, default=0.1
        Epsilon parameter in the epsilon-insensitive loss function.

    class_weight : array, dtype=float64, shape (n_classes,),             default=np.empty(0)
        Set the parameter C of class i to class_weight[i]*C for
        SVC. If not given, all classes are supposed to have
        weight one.

    sample_weight : array, dtype=float64, shape (n_samples,),             default=np.empty(0)
        Weights assigned to each sample.

    shrinking : int, default=1
        Whether to use the shrinking heuristic.

    probability : int, default=0
        Whether to enable probability estimates.

    cache_size : float64, default=100
        Cache size for gram matrix columns (in megabytes).

    max_iter : int (-1 for no limit), default=-1
        Stop solver after this many iterations regardless of accuracy
        (XXX Currently there is no API to know whether this kicked in.)

    random_seed : int, default=0
        Seed for the random number generator used for probability estimates.

    Returns
    -------
    target : array, float

    
    Predict margin (libsvm name for this is predict_values)

    We have to reconstruct model and parameters to make sure we stay
    in sync with the python object.

    Parameters
    ----------
    X : array-like, dtype=float, size=[n_samples, n_features]

    support : array, shape=[n_support]
        Index of support vectors in training set.

    SV : array, shape=[n_support, n_features]
        Support vectors.

    nSV : array, shape=[n_class]
        Number of support vectors in each class.

    sv_coef : array, shape=[n_class-1, n_support]
        Coefficients of support vectors in decision function.

    intercept : array, shape=[n_class*(n_class-1)/2]
        Intercept in decision function.

    probA, probB : array, shape=[n_class*(n_class-1)/2]
        Probability estimates.

    svm_type : {0, 1, 2, 3, 4}, optional
        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
        respectively. 0 by default.

    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, optional
        Kernel to use in the model: linear, polynomial, RBF, sigmoid
        or precomputed. 'rbf' by default.

    degree : int32, optional
        Degree of the polynomial kernel (only relevant if kernel is
        set to polynomial), 3 by default.

    gamma : float64, optional
        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
        kernels. 0.1 by default.

    coef0 : float64, optional
        Independent parameter in poly/sigmoid kernel. 0 by default.

    Returns
    -------
    dec_values : array
        Predicted values.
    
    Predict probabilities

    svm_model stores all parameters needed to predict a given value.

    For speed, all real work is done at the C level in function
    copy_predict (libsvm_helper.c).

    We have to reconstruct model and parameters to make sure we stay
    in sync with the python object.

    See sklearn.svm.predict for a complete list of parameters.

    Parameters
    ----------
    X : array-like, dtype=float of shape (n_samples, n_features)

    support : array of shape (n_support,)
        Index of support vectors in training set.

    SV : array of shape (n_support, n_features)
        Support vectors.

    nSV : array of shape (n_class,)
        Number of support vectors in each class.

    sv_coef : array of shape (n_class-1, n_support)
        Coefficients of support vectors in decision function.

    intercept : array of shape (n_class*(n_class-1)/2,)
        Intercept in decision function.

    probA, probB : array of shape (n_class*(n_class-1)/2,)
        Probability estimates.

    svm_type : {0, 1, 2, 3, 4}, default=0
        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
        respectively.

    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
        Kernel to use in the model: linear, polynomial, RBF, sigmoid
        or precomputed.

    degree : int32, default=3
        Degree of the polynomial kernel (only relevant if kernel is
        set to polynomial).

    gamma : float64, default=0.1
        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
        kernels.

    coef0 : float64, default=0.0
        Independent parameter in poly/sigmoid kernel.

    Returns
    -------
    dec_values : array
        Predicted values.
    
    Predict target values of X given a model (low-level method)

    Parameters
    ----------
    X : array-like, dtype=float of shape (n_samples, n_features)

    support : array of shape (n_support,)
        Index of support vectors in training set.

    SV : array of shape (n_support, n_features)
        Support vectors.

    nSV : array of shape (n_class,)
        Number of support vectors in each class.

    sv_coef : array of shape (n_class-1, n_support)
        Coefficients of support vectors in decision function.

    intercept : array of shape (n_class*(n_class-1)/2)
        Intercept in decision function.

    probA, probB : array of shape (n_class*(n_class-1)/2,)
        Probability estimates.

    svm_type : {0, 1, 2, 3, 4}, default=0
        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
        respectively.

    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
        Kernel to use in the model: linear, polynomial, RBF, sigmoid
        or precomputed.

    degree : int32, default=3
        Degree of the polynomial kernel (only relevant if kernel is
        set to polynomial).

    gamma : float64, default=0.1
        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
        kernels.

    coef0 : float64, default=0.0
        Independent parameter in poly/sigmoid kernel.

    Returns
    -------
    dec_values : array
        Predicted values.
    
    Train the model using libsvm (low-level method)

    Parameters
    ----------
    X : array-like, dtype=float64 of shape (n_samples, n_features)

    Y : array, dtype=float64 of shape (n_samples,)
        target vector

    svm_type : {0, 1, 2, 3, 4}, default=0
        Type of SVM: C_SVC, NuSVC, OneClassSVM, EpsilonSVR or NuSVR
        respectively.

    kernel : {'linear', 'rbf', 'poly', 'sigmoid', 'precomputed'}, default="rbf"
        Kernel to use in the model: linear, polynomial, RBF, sigmoid
        or precomputed.

    degree : int32, default=3
        Degree of the polynomial kernel (only relevant if kernel is
        set to polynomial).

    gamma : float64, default=0.1
        Gamma parameter in rbf, poly and sigmoid kernels. Ignored by other
        kernels.

    coef0 : float64, default=0
        Independent parameter in poly/sigmoid kernel.

    tol : float64, default=1e-3
        Numeric stopping criterion (WRITEME).

    C : float64, default=1
        C parameter in C-Support Vector Classification.

    nu : float64, default=0.5
        An upper bound on the fraction of training errors and a lower bound of
        the fraction of support vectors. Should be in the interval (0, 1].

    epsilon : double, default=0.1
        Epsilon parameter in the epsilon-insensitive loss function.

    class_weight : array, dtype=float64, shape (n_classes,),             default=np.empty(0)
        Set the parameter C of class i to class_weight[i]*C for
        SVC. If not given, all classes are supposed to have
        weight one.

    sample_weight : array, dtype=float64, shape (n_samples,),             default=np.empty(0)
        Weights assigned to each sample.

    shrinking : int, default=1
        Whether to use the shrinking heuristic.

    probability : int, default=0
        Whether to enable probability estimates.

    cache_size : float64, default=100
        Cache size for gram matrix columns (in megabytes).

    max_iter : int (-1 for no limit), default=-1
        Stop solver after this many iterations regardless of accuracy
        (XXX Currently there is no API to know whether this kicked in.)

    random_seed : int, default=0
        Seed for the random number generator used for probability estimates.

    Returns
    -------
    support : array of shape (n_support,)
        Index of support vectors.

    support_vectors : array of shape (n_support, n_features)
        Support vectors (equivalent to X[support]). Will return an
        empty array in the case of precomputed kernel.

    n_class_SV : array of shape (n_class,)
        Number of support vectors in each class.

    sv_coef : array of shape (n_class-1, n_support)
        Coefficients of support vectors in decision function.

    intercept : array of shape (n_class*(n_class-1)/2,)
        Intercept in decision function.

    probA, probB : array of shape (n_class*(n_class-1)/2,)
        Probability estimates, empty array for probability=False.
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