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

    neighbors : array-like, shape (n_samples, K) or None
        Each row contains the indices to the K nearest neigbors. If this
        array is None, then the perplexity is estimated over all data
        not just the nearest neighbors.

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

    verbose : int
        Verbosity level.

    Returns
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    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_perplexityRuntimeErroraffinitiesValueErrorneighborsverbosefloat64__import__zerosrangeprintnumpydtype__test__mean__main__fileendnpZgZfZdqlihgfdbQOLIHBÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿàÐ @Ð ÐÐ ÀÏ 0Ñ  Æ <
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