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from .knn import KNN
class DTM:
def __init__(self, k, q=2, **kwargs):
"""
Args:
q (float): order used to compute the distance to measure. Defaults to the dimension, or 2 if input_type is 'distance_matrix'.
kwargs: Same parameters as KNN, except that metric="neighbors" means that transform() expects an array with the distances to the k nearest neighbors.
"""
self.k = k
self.q = q
self.params = kwargs
def fit_transform(self, X, y=None):
return self.fit(X).transform(X)
def fit(self, X, y=None):
"""
Args:
X (numpy.array): coordinates for mass points
"""
if self.params.setdefault("metric", "euclidean") != "neighbors":
self.knn = KNN(self.k, return_index=False, return_distance=True, **self.params)
self.knn.fit(X)
return self
def transform(self, X):
"""
Args:
X (numpy.array): coordinates for query points, or distance matrix if metric is "precomputed", or distances to the k nearest neighbors if metric is "neighbors" (if the array has more than k columns, the remaining ones are ignored).
"""
if self.params["metric"] == "neighbors":
distances = X[:, : self.k]
else:
distances = self.knn.transform(X)
distances = distances ** self.q
dtm = distances.sum(-1) / self.k
dtm = dtm ** (1.0 / self.q)
return dtm
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