A t-Distributed Stochastic Neighbor Embedding implementation. Parameters ---------- max_iter : int, default 200 perplexity : float, default 30.0 n_components : int, default 2
(
self, n_components=2, perplexity=30.0, max_iter=200, learning_rate=500
)
| 20 | y_required = False |
| 21 | |
| 22 | def __init__( |
| 23 | self, n_components=2, perplexity=30.0, max_iter=200, learning_rate=500 |
| 24 | ): |
| 25 | """A t-Distributed Stochastic Neighbor Embedding implementation. |
| 26 | |
| 27 | Parameters |
| 28 | ---------- |
| 29 | max_iter : int, default 200 |
| 30 | perplexity : float, default 30.0 |
| 31 | n_components : int, default 2 |
| 32 | """ |
| 33 | self.max_iter = max_iter |
| 34 | self.perplexity = perplexity |
| 35 | self.n_components = n_components |
| 36 | self.initial_momentum = 0.5 |
| 37 | self.final_momentum = 0.8 |
| 38 | self.min_gain = 0.01 |
| 39 | self.lr = learning_rate |
| 40 | self.tol = 1e-5 |
| 41 | self.perplexity_tries = 50 |
| 42 | |
| 43 | def fit_transform(self, X, y=None): |
| 44 | self._setup_input(X, y) |
nothing calls this directly
no outgoing calls
no test coverage detected