evomap.mapping._tsne
Contents
evomap.mapping._tsne
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T-Distributed Stochastic Neighborhood Embedding, as propsoed in:
Van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of machine learning research, 9(11).
Module Contents#
Classes#
Functions#
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Check and, if necessary, prepare data for t-SNE. |
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Calculate Q-Matrix of joint probabilities in low-dim space. |
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Calculate gradient of KL-divergence dC/dY. |
Take an asymmetric conditional probability matrix and convert it to a |
Attributes#
- evomap.mapping._tsne.EPSILON = 1e-12#
- class evomap.mapping._tsne.TSNE(n_dims=2, perplexity=15, n_iter=2000, stop_lying_iter=250, early_exaggeration=4, initial_momentum=0.5, final_momentum=0.8, eta='auto', n_iter_check=50, init=None, verbose=0, input_type='distance', maxhalves=5, tol=0.001, n_inits=1, step_size=1)[source]#
- evomap.mapping._tsne._check_prepare_tsne(model, X)[source]#
Check and, if necessary, prepare data for t-SNE.
- evomap.mapping._tsne.calc_q_matrix(Y, inclusions)[source]#
Calculate Q-Matrix of joint probabilities in low-dim space.
- Parameters
-- (Y {np.ndarray}) –
exclusions (exclusions {np.ndarray} -- condensed-dist-mat indices for) –
- Returns
Q {np.ndarray} – (n,n) array of joint probabilities in low-dim space. dist {np.ndarray} – (n,n) array of squared euclidean distances
- evomap.mapping._tsne._kl_divergence_grad(Y, P, Q, dist)[source]#
Calculate gradient of KL-divergence dC/dY.
- Parameters
-- (Y {np.ndarray}) –
probabilities (Q {np.ndarray} -- condensed-matrix of joint) –
probabilities –
distances (dist {np.ndarray} -- condensed-matrix of sq.euclidean) –
- Returns
dY {np.ndarray} – (n,2) array of gradient values