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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')sklearn/decomposition/_online_lda_fast.c%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'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/decomposition/_online_lda_fast.pyxsklearn.decomposition._online_lda_fast.mean_changesklearn.decomposition._online_lda_fast._dirichlet_expectation_1dCannot convert %.200s to %.200ssklearn.decomposition._online_lda_fast._dirichlet_expectation_2dArgument '%.200s' has incorrect type (expected %.200s, got %.200s)%.200s() takes %.8s %zd positional argument%.1s (%zd given)sklearn.decomposition._online_lda_fastcompiletime 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 runtimecalling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptioninit sklearn.decomposition._online_lda_fast@ð?à?AAp??UUUUUUµ?�÷ư>¶oüŒxâ¿ð¿ÿÿÿÿÿÿÿ
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    E[log(theta)] for theta ~ Dir(arr).

    Equivalent to psi(arr) - psi(np.sum(arr, axis=1))[:, np.newaxis].

    Note that unlike _dirichlet_expectation_1d, this function doesn't compute
    the exp and doesn't add in the prior.
    Dirichlet expectation for a single sample:
        exp(E[log(theta)]) for theta ~ Dir(doc_topic)
    after adding doc_topic_prior to doc_topic, in-place.

    Equivalent to
        doc_topic += doc_topic_prior
        out[:] = np.exp(psi(doc_topic) - psi(np.sum(doc_topic)))
    Calculate the mean difference between two arrays.

    Equivalent to np.abs(arr_1 - arr2).mean().
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