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CÀ 0ؘ@	þÿÿoHÿÿÿoðÿÿo„ùÿÿo@`½ F!V!f!v!†!–!¦!¶!Æ!Ö!æ!ö!""&"6"F"V"f"v"†"–"¦"¶"Æ"Ö"æ"ö"##&#6#F#V#f#v#†#–#¦#¶#Æ#Ö#æ#ö#$$&$6$F$V$f$v$†$–$¦$¶$Æ$Ö$æ$ö$%%&%6%F%V%@ Binary search for sigmas of conditional Gaussians.

    We are looking for sigma = sqrt(1/beta) so that the perplexity
    of the conditional distribution p_i|j matches approximately the
    desired value. p_i|j are Gaussian distributed.

    The idea has been proposed in "Stochastic Neighbor Embedding"
    Geoffrey Hinton and Sam Roweis, 2003.

    Parameters
    ----------
    affinities : array-like, shape (n_samples, n_samples)
        Distances between training samples.

    desired_perplexity : double
        Desired perplexity (2^entropy) of the conditional Gaussians.

    verbose : int
        Verbosity level.

    Returns
    -------
    P : array, shape (n_samples, n_samples)
        Probabilities of conditional Gaussian distributions p_i|j.
    Format string allocated too short.ndarray is not Fortran contiguousNon-native byte order not supportedFormat string allocated too short, see comment in numpy.pxdunknown dtype code in numpy.pxd (%d)[t-SNE] Computed conditional probabilities for sample %d / %dndarray is not C contiguous[t-SNE] Mean sigma: %fdesired_perplexity__pyx_releasebuffer__pyx_getbufferRuntimeErroraffinitiesValueErrorverbose__import__doublerangeprintnumpydtype__test__mean__main__fileendnpZgZfZdqlihgfdbQOLIHBÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿPÏ @Ï 0Î  Ï  Æ <
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