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scikit-learn / linear_model / _sag_fast.cpython-310-aarch64-linux-gnu.so
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´@@ùá´àª ?Öøª`´ÓGù@ù_ëaTàª§¿ÿ@ùàÛ¹ Ñù@èÿµàª8«ÿ?ÿÿ! Lµÿøª@þÿµnÿÿ0@ù@´@@ù´àª ?Öøª´ÓGù@ù_ëaTàª¿ÿ@ùàã¹ ÑùÚÿµàª«ÿÍþÿ! 0µÿøª@þÿµüþÿ 0@ù`´@@ù!´àª ?Ö÷ª ´ÓGù@ù_ëaTàªo¿ÿá@ùàç¹ ÑàùÀËÿµàª«ÿ[þÿ! µÿ÷ª@þÿµþÿp«ÿÑÿµ Ð!@°Gù@ùâç¹«ÿJþÿe«ÿ`Úÿµ Ð!@°Gù@ùâã¹«ÿþÿZ«ÿÀãÿµ Ð!@°Gù@ùâÛ¹«ÿàþÿÅ°°ùÿý{¿©ýý{hÀ_Ö while calling a Python objectNULL result without error in PyObject_Call__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)intan integer is required%.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 PyObjectinvalid vtable found for imported typeInterpreter change detected - this module can only be loaded into one interpreter per process.name__loader__loader__file__origin__package__parent__path__submodule_search_locations%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'Unexpected format string character: '%c'join() result is too long for a Python stringcalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionMissing type objectCannot convert %.200s to %.200sArgument '%.200s' has incorrect type (expected %.200s, got %.200s)name '%U' is not definedend'unparseable format string'complex double''signed char''unsigned char''short''unsigned short''int''unsigned int''long''unsigned long''long long''unsigned long long''double''complex long double''bool''char''complex float''float'a structPython objecta pointera string'long double'Buffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'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 stringDoes not understand character buffer dtype format string ('%c')Expected 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)buffer dtypeItem size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)value too large to convert to intsklearn/linear_model/_sag_fast.c%s (%s:%d)Module '_sag_fast' has already been imported. Re-initialisation is not supported.%d.%dsklearn.linear_model._sag_fastcompiletime version %s of module '%.100s' does not match runtime version %sbuiltinscython_runtime__builtins__numpydtypeflatiterbroadcastndarraygenericnumbersignedintegerunsignedintegerinexactfloatingcomplexfloatingflexiblecharacterufuncsklearn.linear_model._sgd_fastLossFunctionRegressionClassificationLogSquaredLosssklearn.utils._seq_datasetSequentialDataset64ArrayDataset64CSRDataset64SequentialDataset32ArrayDataset32CSRDataset32numpy.core._multiarray_umath_ARRAY_API_ARRAY_API not found_ARRAY_API is not PyCapsule object_ARRAY_API is NULL pointermodule 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 runtime__init__.pxdnumpy.import_arraysklearn/linear_model/_sag_fast.pyxinit sklearn.linear_model._sag_fastsklearn.linear_model._sag_fast.MultinomialLogLoss32.__reduce__sklearn.linear_model._sag_fast.MultinomialLogLoss64.__reduce__sklearn.linear_model._sag_fast._multinomial_grad_loss_all_samplesexactly_multinomial_grad_loss_all_samples%.200s() takes %.8s %zd positional argument%.1s (%zd given)datasetweights_arrayintercept_arraymultilosslocal variable '%s' referenced before assignmentlossEpoch %d, change: %.8f
sklearn.linear_model._sag_fast.sag32sag32loss_functionsum_gradient_initgradient_memory_initseen_initintercept_sum_gradient_initsklearn.linear_model._sag_fast.sag64sag64_sag_fastsklearn.linear_model._sag_fast.MultinomialLogLoss32__reduce__sklearn.linear_model._sag_fast.MultinomialLogLoss64floatbintdoubleJóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóóPóóMóóóóó25óó8óó;>AóóDóóóóóóóóóóóóóGSó%,óóóóó/órÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿoÜÿÜÿÜÿÜÿÜÿlÜÿÜÿÜÿÜÿ~{ÜÿÜÿxÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿÜÿueÜÿ^WTQÜÿÜÿNÜÿÜÿÜÿKÜÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿQ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿQ>ÿ>ÿ>ÿ>ÿ>ÿ>ÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ=áÿáÿ=áÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ=Báÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿáÿ5áÿ?B#?áÿ#áÿáÿáÿáÿáÿ##áÿáÿ#áÿáÿ#áÿ#áÿáÿiáÿáÿáÿáÿáÿOáÿáÿáÿ?áÿáÿáÿ###áÿ####áÿáÿ#áÿáÿáÿ##áÿûáÿáÿáÿáÿªáÿáÿáÿáÿEÿü&$" 
LþUÏñïíëçåãáßÝÛÙ×ÕÓÑéÍEÿü&$" 
LþUÏñïíëçåãáßÝÛÙ×ÕÓÑéÍ00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899sklearn/linear_model/_sag_fast.pyxnumpy.core.umath failed to importmax_iter reached after %d secondsInvalid loss parameter: got %s instead of one of ('log', 'squared', 'multinomial')Floating-point under-/overflow occurred at epoch #%d. Scaling input data with StandardScaler or MinMaxScaler might help.numpy.core.multiarray failed to import_multinomial_grad_loss_all_samples
SAG and SAGA implementation
WARNING: Do not edit .pyx file directly, it is generated from .pyx.tp
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    Used for testing purpose only.
    Stochastic Average Gradient (SAG) and SAGA solvers.

    Used in Ridge and LogisticRegression.

    Reference
    ---------
    Schmidt, M., Roux, N. L., & Bach, F. (2013).
    Minimizing finite sums with the stochastic average gradient
    https://hal.inria.fr/hal-00860051/document
    (section 4.3)

    :arxiv:`Defazio, A., Bach F. & Lacoste-Julien S. (2014).
    "SAGA: A Fast Incremental Gradient Method With Support
    for Non-Strongly Convex Composite Objectives" <1407.0202>`
    Stochastic Average Gradient (SAG) and SAGA solvers.

    Used in Ridge and LogisticRegression.

    Reference
    ---------
    Schmidt, M., Roux, N. L., & Bach, F. (2013).
    Minimizing finite sums with the stochastic average gradient
    https://hal.inria.fr/hal-00860051/document
    (section 4.3)

    :arxiv:`Defazio, A., Bach F. & Lacoste-Julien S. (2014).
    "SAGA: A Fast Incremental Gradient Method With Support
    for Non-Strongly Convex Composite Objectives" <1407.0202>`
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