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umap_var.py
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umap_var.py
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import numpy as np
import utils as u
import time
class UMAP:
def __init__(self,
n_components=2,
n_neighbors=5,
num_trees=5,
spread=1.,
min_dist=0.1,
learning_rate=1.0,
repulsion_strength=1.0,
negative_sample_rate=5,
init='random' # only
):
self.n_components = n_components
self.n_neighbors = n_neighbors
self.num_trees = num_trees
self.spread = spread
self.min_dist = min_dist
self._initial_alpha = learning_rate
self.repulsion_strength = repulsion_strength
self.negative_sample_rate = negative_sample_rate
self.init = init
self._a, self._b = None, None
# self.r_forest = None
self.sigmas = None
self.rhos = None
self.graph = None
self.embedding = None
def fit(self, X, counts=None):
t0 = time.time()
self._a, self._b = u.find_ab_params(self.spread, self.min_dist)
print(time.time() - t0)
t0 = time.time()
self.graph, self.sigmas, self.rhos = u.build_graph_nocoo(X, self.n_neighbors, counts)
print(time.time() - t0)
t0 = time.time()
self.embedding = u.embed_graph(
self.graph,
X.shape[0],
self.n_components,
self._initial_alpha, # self.learning_rate
self._a,
self._b,
self.repulsion_strength, # repulsive strength
self.negative_sample_rate,
n_epochs=0,
init=self.init
)
print(time.time() - t0)
def fit_transform(self, X, counts=None):
self.fit(X, counts)
return self.embedding
def transform(self):
pass