class KDEIsomapGen(GenBase):
# TODO: Isomap has no inverse transformation, maybe we can solve that in the future.
# TODO: Look the the work: Inverse Methods for Manifold Learning from Kathleen Kay
def __init__(self,
kernel="gaussian",
bandwidth=0.1,
n_components=None,
n_neighbors=20):
super().__init__()
self.transformer = Isomap(n_neighbors, n_components=n_components)
self.bandwidth = bandwidth
self.kernel = kernel
self.manifold = None
raise NotImplementedError
def fit(self, x):
x_pca = self.transformer.fit_transform(x)
self.manifold = KDEGen(kernel=self.kernel,
bandwidth=self.bandwidth).fit(x_pca)
return self
def sample_radius(self,
x_exp,
n_min_kernels=20,
r=None,
n_samples=1,
random_state=None):
x_exp_pca = self.transformer.transform(x_exp)
x_sample_pca = self.manifold.sample_radius(x_exp_pca,
n_min_kernels=n_min_kernels,
r=r,
n_samples=n_samples,
random_state=random_state)
x_sample = self.transformer.inverse_transform(x_sample_pca)
return x_sample
def sample(self, n_samples=1, random_state=None):
x_sample_pca = self.manifold.sample(n_samples=n_samples,
random_state=random_state)
x_sample = self.transformer.inverse_transform(x_sample_pca)
return x_sample
