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XvM G>/e+#5(â$wQ@аk{(C6i`{q8P[,G:N H?ȡz#FF"*{x4$|C>8 #w"  .$C&$j@J$`tAY탷JV=#0׀C\ya~\q-3H-FE0G($,E>""hpIVbVh0 ru$HTJF'>%mQ%]{ J$#!#=:rpS3ćH}hi&WG-}θBހ''SwɊ8PZmLjѵDFԥD&}A[V|ԄPM9LMnͅpyPҩ " tLD T6ifY;-},`^Yh+X4^j2l`nKܔ{mC6fܪz&&"=*8W.mkc=Q;(G;\d@limHE[r>~aH3Wխp3G/~C7=[R͔rxŅ ڄfS/h|*Fm+ b!SmC/"h'0\'0sKQHd9O ǐVԟI$$aF!b͊ * :|TL $ߤ/Z>=$d;=;7_< zߧx8ٻ`\Ã/38MjX\oD }Ѝ__pyx_fatalerror00010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899Binary search for sigmas of conditional Gaussians. This approximation reduces the computational complexity from O(N^2) to O(uN). Parameters ---------- sqdistances : ndarray of shape (n_samples, n_neighbors), dtype=np.float32 Distances between training samples and their k nearest neighbors. When using the exact method, this is a square (n_samples, n_samples) distance matrix. The TSNE default metric is "euclidean" which is interpreted as squared euclidean distance. desired_perplexity : float Desired perplexity (2^entropy) of the conditional Gaussians. verbose : int Verbosity level. 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