Merge branch 'master' of https://github.com/GUDHI/gudhi-devel

6 files changed, 195 insertions, 41 deletions

diff --git a/src/python/doc/point_cloud_sum.inc b/src/python/doc/point_cloud_sum.inc
index 4315cea6..f955c3ab 100644
--- a/src/python/doc/point_cloud_sum.inc
+++ b/src/python/doc/point_cloud_sum.inc
@@ -3,8 +3,8 @@
 
    +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+
    | | :math:`(x_1, x_2, \ldots, x_d)` | Utilities to process point clouds: read from file, subsample, | :Authors: Vincent Rouvreau, Marc Glisse, Masatoshi Takenouchi     |
-   | | :math:`(y_1, y_2, \ldots, y_d)` | find neighbors, embed time series in higher dimension, etc.   |                                                                   |
-   |                                   |                                                               | :Since: GUDHI 2.0.0                                               |
+   | | :math:`(y_1, y_2, \ldots, y_d)` | find neighbors, embed time series in higher dimension,        |                                                                   |
+   |                                   | estimate a density, etc.                                      | :Since: GUDHI 2.0.0                                               |
    |                                   |                                                               |                                                                   |
    |                                   |                                                               | :License: MIT (`GPL v3 </licensing/>`_, BSD-3-Clause, Apache-2.0) |
    +-----------------------------------+---------------------------------------------------------------+-------------------------------------------------------------------+
diff --git a/src/python/gudhi/cubical_complex.pyx b/src/python/gudhi/cubical_complex.pyx
index ff0a9ed8..28fbe3af 100644
--- a/src/python/gudhi/cubical_complex.pyx
+++ b/src/python/gudhi/cubical_complex.pyx
@@ -27,20 +27,20 @@ __license__ = "MIT"
 
 cdef extern from "Cubical_complex_interface.h" namespace "Gudhi":
     cdef cppclass Bitmap_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<>":
-        Bitmap_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells)
-        Bitmap_cubical_complex_base_interface(string perseus_file)
-        int num_simplices()
-        int dimension()
+        Bitmap_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells) nogil
+        Bitmap_cubical_complex_base_interface(string perseus_file) nogil
+        int num_simplices() nogil
+        int dimension() nogil
 
 cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
     cdef cppclass Cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<>>":
-        Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max)
-        void compute_persistence(int homology_coeff_field, double min_persistence)
-        vector[pair[int, pair[double, double]]] get_persistence()
-        vector[vector[int]] cofaces_of_cubical_persistence_pairs()
-        vector[int] betti_numbers()
-        vector[int] persistent_betti_numbers(double from_value, double to_value)
-        vector[pair[double,double]] intervals_in_dimension(int dimension)
+        Cubical_complex_persistence_interface(Bitmap_cubical_complex_base_interface * st, bool persistence_dim_max) nogil
+        void compute_persistence(int homology_coeff_field, double min_persistence) nogil
+        vector[pair[int, pair[double, double]]] get_persistence() nogil
+        vector[vector[int]] cofaces_of_cubical_persistence_pairs() nogil
+        vector[int] betti_numbers() nogil
+        vector[int] persistent_betti_numbers(double from_value, double to_value) nogil
+        vector[pair[double,double]] intervals_in_dimension(int dimension) nogil
 
 # CubicalComplex python interface
 cdef class CubicalComplex:
@@ -80,7 +80,7 @@ cdef class CubicalComplex:
                   perseus_file=''):
         if ((dimensions is not None) and (top_dimensional_cells is not None)
             and (perseus_file == '')):
-            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+            self._construct_from_cells(dimensions, top_dimensional_cells)
         elif ((dimensions is None) and (top_dimensional_cells is not None)
             and (perseus_file == '')):
             top_dimensional_cells = np.array(top_dimensional_cells,
@@ -88,11 +88,11 @@ cdef class CubicalComplex:
                                              order = 'F')
             dimensions = top_dimensional_cells.shape
             top_dimensional_cells = top_dimensional_cells.ravel(order='F')
-            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+            self._construct_from_cells(dimensions, top_dimensional_cells)
         elif ((dimensions is None) and (top_dimensional_cells is None)
             and (perseus_file != '')):
             if os.path.isfile(perseus_file):
-                self.thisptr = new Bitmap_cubical_complex_base_interface(perseus_file.encode('utf-8'))
+                self._construct_from_file(perseus_file.encode('utf-8'))
             else:
                 raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT),
                                         perseus_file)
@@ -101,6 +101,14 @@ cdef class CubicalComplex:
                 "top_dimensional_cells or from a Perseus-style file name.",
                 file=sys.stderr)
 
+    def _construct_from_cells(self, vector[unsigned] dimensions, vector[double] top_dimensional_cells):
+        with nogil:
+            self.thisptr = new Bitmap_cubical_complex_base_interface(dimensions, top_dimensional_cells)
+
+    def _construct_from_file(self, string filename):
+        with nogil:
+            self.thisptr = new Bitmap_cubical_complex_base_interface(filename)
+
     def __dealloc__(self):
         if self.thisptr != NULL:
             del self.thisptr
@@ -151,8 +159,11 @@ cdef class CubicalComplex:
         if self.pcohptr != NULL:
             del self.pcohptr
         assert self.__is_defined()
-        self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, True)
-        self.pcohptr.compute_persistence(homology_coeff_field, min_persistence)
+        cdef int field = homology_coeff_field
+        cdef double minp = min_persistence
+        with nogil:
+            self.pcohptr = new Cubical_complex_persistence_interface(self.thisptr, 1)
+            self.pcohptr.compute_persistence(field, minp)
 
     def persistence(self, homology_coeff_field=11, min_persistence=0):
         """This function computes and returns the persistence of the complex.
@@ -204,7 +215,8 @@ cdef class CubicalComplex:
 
         cdef vector[vector[int]] persistence_result
         output = [[],[]]
-        persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
+        with nogil:
+            persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
         pr = np.array(persistence_result)
 
         ess_ind = np.argwhere(pr[:,2] == -1)[:,0]
diff --git a/src/python/gudhi/periodic_cubical_complex.pyx b/src/python/gudhi/periodic_cubical_complex.pyx
index 06309772..bed55101 100644
--- a/src/python/gudhi/periodic_cubical_complex.pyx
+++ b/src/python/gudhi/periodic_cubical_complex.pyx
@@ -24,20 +24,20 @@ __license__ = "MIT"
 
 cdef extern from "Cubical_complex_interface.h" namespace "Gudhi":
     cdef cppclass Periodic_cubical_complex_base_interface "Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>":
-        Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions)
-        Periodic_cubical_complex_base_interface(string perseus_file)
-        int num_simplices()
-        int dimension()
+        Periodic_cubical_complex_base_interface(vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions) nogil
+        Periodic_cubical_complex_base_interface(string perseus_file) nogil
+        int num_simplices() nogil
+        int dimension() nogil
 
 cdef extern from "Persistent_cohomology_interface.h" namespace "Gudhi":
     cdef cppclass Periodic_cubical_complex_persistence_interface "Gudhi::Persistent_cohomology_interface<Gudhi::Cubical_complex::Cubical_complex_interface<Gudhi::cubical_complex::Bitmap_cubical_complex_periodic_boundary_conditions_base<double>>>":
-        Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max)
-        void compute_persistence(int homology_coeff_field, double min_persistence)
-        vector[pair[int, pair[double, double]]] get_persistence()
-        vector[vector[int]] cofaces_of_cubical_persistence_pairs()
-        vector[int] betti_numbers()
-        vector[int] persistent_betti_numbers(double from_value, double to_value)
-        vector[pair[double,double]] intervals_in_dimension(int dimension)
+        Periodic_cubical_complex_persistence_interface(Periodic_cubical_complex_base_interface * st, bool persistence_dim_max) nogil
+        void compute_persistence(int homology_coeff_field, double min_persistence) nogil
+        vector[pair[int, pair[double, double]]] get_persistence() nogil
+        vector[vector[int]] cofaces_of_cubical_persistence_pairs() nogil
+        vector[int] betti_numbers() nogil
+        vector[int] persistent_betti_numbers(double from_value, double to_value) nogil
+        vector[pair[double,double]] intervals_in_dimension(int dimension) nogil
 
 # PeriodicCubicalComplex python interface
 cdef class PeriodicCubicalComplex:
@@ -81,9 +81,7 @@ cdef class PeriodicCubicalComplex:
                   periodic_dimensions=None, perseus_file=''):
         if ((dimensions is not None) and (top_dimensional_cells is not None)
             and (periodic_dimensions is not None) and (perseus_file == '')):
-            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions,
-                                                                       top_dimensional_cells,
-                                                                       periodic_dimensions)
+            self._construct_from_cells(dimensions, top_dimensional_cells, periodic_dimensions)
         elif ((dimensions is None) and (top_dimensional_cells is not None)
             and (periodic_dimensions is not None) and (perseus_file == '')):
             top_dimensional_cells = np.array(top_dimensional_cells,
@@ -91,13 +89,11 @@ cdef class PeriodicCubicalComplex:
                                              order = 'F')
             dimensions = top_dimensional_cells.shape
             top_dimensional_cells = top_dimensional_cells.ravel(order='F')
-            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions,
-                                                                       top_dimensional_cells,
-                                                                       periodic_dimensions)
+            self._construct_from_cells(dimensions, top_dimensional_cells, periodic_dimensions)
         elif ((dimensions is None) and (top_dimensional_cells is None)
             and (periodic_dimensions is None) and (perseus_file != '')):
             if os.path.isfile(perseus_file):
-                self.thisptr = new Periodic_cubical_complex_base_interface(perseus_file.encode('utf-8'))
+                self._construct_from_file(perseus_file.encode('utf-8'))
             else:
                 print("file " + perseus_file + " not found.", file=sys.stderr)
         else:
@@ -106,6 +102,14 @@ cdef class PeriodicCubicalComplex:
               "top_dimensional_cells and periodic_dimensions or from "
               "a Perseus-style file name.", file=sys.stderr)
 
+    def _construct_from_cells(self, vector[unsigned] dimensions, vector[double] top_dimensional_cells, vector[bool] periodic_dimensions):
+        with nogil:
+            self.thisptr = new Periodic_cubical_complex_base_interface(dimensions, top_dimensional_cells, periodic_dimensions)
+
+    def _construct_from_file(self, string filename):
+        with nogil:
+            self.thisptr = new Periodic_cubical_complex_base_interface(filename)
+
     def __dealloc__(self):
         if self.thisptr != NULL:
             del self.thisptr
@@ -156,8 +160,11 @@ cdef class PeriodicCubicalComplex:
         if self.pcohptr != NULL:
             del self.pcohptr
         assert self.__is_defined()
-        self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, True)
-        self.pcohptr.compute_persistence(homology_coeff_field, min_persistence)
+        cdef int field = homology_coeff_field
+        cdef double minp = min_persistence
+        with nogil:
+            self.pcohptr = new Periodic_cubical_complex_persistence_interface(self.thisptr, 1)
+            self.pcohptr.compute_persistence(field, minp)
 
     def persistence(self, homology_coeff_field=11, min_persistence=0):
         """This function computes and returns the persistence of the complex.
@@ -207,7 +214,8 @@ cdef class PeriodicCubicalComplex:
         cdef vector[vector[int]] persistence_result
         if self.pcohptr != NULL:
             output = [[],[]]
-            persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
+            with nogil:
+                persistence_result = self.pcohptr.cofaces_of_cubical_persistence_pairs()
             pr = np.array(persistence_result)
 
             ess_ind = np.argwhere(pr[:,2] == -1)[:,0]
diff --git a/src/python/gudhi/point_cloud/dtm.py b/src/python/gudhi/point_cloud/dtm.py
index 13e16d24..55ac58e6 100644
--- a/src/python/gudhi/point_cloud/dtm.py
+++ b/src/python/gudhi/point_cloud/dtm.py
@@ -8,6 +8,7 @@
 #   - YYYY/MM Author: Description of the modification
 
 from .knn import KNearestNeighbors
+import numpy as np
 
 __author__ = "Marc Glisse"
 __copyright__ = "Copyright (C) 2020 Inria"
@@ -68,3 +69,111 @@ class DistanceToMeasure:
         # We compute too many powers, 1/p in knn then q in dtm, 1/q in dtm then q or some log in the caller.
         # Add option to skip the final root?
         return dtm
+
+
+class DTMDensity:
+    """
+    Density estimator based on the distance to the empirical measure defined by a point set, as defined
+    in :cite:`dtmdensity`. Note that this implementation only renormalizes when asked, and the renormalization
+    only works for a Euclidean metric, so in other cases the total measure may not be 1.
+
+    .. note:: When the dimension is high, using it as an exponent can quickly lead to under- or overflows.
+        We recommend using a small fixed value instead in those cases, even if it won't have the same nice
+        theoretical properties as the dimension.
+    """
+
+    def __init__(self, k=None, weights=None, q=None, dim=None, normalize=False, n_samples=None, **kwargs):
+        """
+        Args:
+            k (int): number of neighbors (possibly including the point itself). Optional if it can be guessed
+                from weights or metric="neighbors".
+            weights (numpy.array): weights of each of the k neighbors, optional. They are supposed to sum to 1.
+            q (float): order used to compute the distance to measure. Defaults to dim.
+            dim (float): final exponent representing the dimension. Defaults to the dimension, and must be specified
+                when the dimension cannot be read from the input (metric is "neighbors" or "precomputed").
+            normalize (bool): normalize the density so it corresponds to a probability measure on ℝᵈ.
+                Only available for the Euclidean metric, defaults to False.
+            n_samples (int): number of sample points used for fitting. Only needed if `normalize` is True and
+                metric is "neighbors".
+            kwargs: same parameters as :class:`~gudhi.point_cloud.knn.KNearestNeighbors`, except that
+                metric="neighbors" means that :func:`transform` expects an array with the distances to
+                the k nearest neighbors.
+        """
+        if weights is None:
+            self.k = k
+            if k is None:
+                assert kwargs.get("metric") == "neighbors", 'Must specify k or weights, unless metric is "neighbors"'
+                self.weights = None
+            else:
+                self.weights = np.full(k, 1.0 / k)
+        else:
+            self.weights = weights
+            self.k = len(weights)
+            assert k is None or k == self.k, "k differs from the length of weights"
+        self.q = q
+        self.dim = dim
+        self.params = kwargs
+        self.normalize = normalize
+        self.n_samples = n_samples
+
+    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 = KNearestNeighbors(
+                self.k, return_index=False, return_distance=True, sort_results=False, **self.params
+            )
+            self.knn.fit(X)
+            if self.params["metric"] != "precomputed":
+                self.n_samples = len(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).
+        """
+        q = self.q
+        dim = self.dim
+        if dim is None:
+            assert self.params["metric"] not in {
+                "neighbors",
+                "precomputed",
+            }, "dim not specified and cannot guess the dimension"
+            dim = len(X[0])
+        if q is None:
+            q = dim
+        k = self.k
+        weights = self.weights
+        if self.params["metric"] == "neighbors":
+            distances = np.asarray(X)
+            if weights is None:
+                k = distances.shape[1]
+                weights = np.full(k, 1.0 / k)
+            else:
+                distances = distances[:, :k]
+        else:
+            distances = self.knn.transform(X)
+        distances = distances ** q
+        dtm = (distances * weights).sum(-1)
+        if self.normalize:
+            dtm /= (np.arange(1, k + 1) ** (q / dim) * weights).sum()
+        density = dtm ** (-dim / q)
+        if self.normalize:
+            import math
+
+            if self.params["metric"] == "precomputed":
+                self.n_samples = len(X[0])
+            # Volume of d-ball
+            Vd = math.pi ** (dim / 2) / math.gamma(dim / 2 + 1)
+            density /= self.n_samples * Vd
+        return density
+        # We compute too many powers, 1/p in knn then q in dtm, d/q in dtm then whatever in the caller.
+        # Add option to skip the final root?
diff --git a/src/python/gudhi/point_cloud/knn.py b/src/python/gudhi/point_cloud/knn.py
index 86008bc3..4652fe80 100644
--- a/src/python/gudhi/point_cloud/knn.py
+++ b/src/python/gudhi/point_cloud/knn.py
@@ -306,6 +306,10 @@ class KNearestNeighbors:
         if self.params["implementation"] == "ckdtree":
             qargs = {key: val for key, val in self.params.items() if key in {"p", "eps", "n_jobs"}}
             distances, neighbors = self.kdtree.query(X, k=self.k, **qargs)
+            if k == 1:
+                # SciPy decided to squeeze the last dimension for k=1
+                distances = distances[:, None]
+                neighbors = neighbors[:, None]
             if self.return_index:
                 if self.return_distance:
                     return neighbors, distances
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index bff4c267..0a52279e 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -8,10 +8,11 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.dtm import DistanceToMeasure
+from gudhi.point_cloud.dtm import DistanceToMeasure, DTMDensity
 import numpy
 import pytest
 import torch
+import math
 
 
 def test_dtm_compare_euclidean():
@@ -66,3 +67,23 @@ def test_dtm_precomputed():
     dtm = DistanceToMeasure(2, q=2, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01)
+
+
+def test_density_normalized():
+    sample = numpy.random.normal(0, 1, (1000000, 2))
+    queries = numpy.array([[0.0, 0.0], [-0.5, 0.7], [0.4, 1.7]])
+    expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi)
+    estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries)
+    assert estimated == pytest.approx(expected, rel=0.4)
+
+
+def test_density():
+    distances = [[0, 1, 10], [2, 0, 30], [1, 3, 5]]
+    density = DTMDensity(k=2, metric="neighbors", dim=1).fit_transform(distances)
+    expected = numpy.array([2.0, 1.0, 0.5])
+    assert density == pytest.approx(expected)
+    distances = [[0, 1], [2, 0], [1, 3]]
+    density = DTMDensity(metric="neighbors", dim=1).fit_transform(distances)
+    assert density == pytest.approx(expected)
+    density = DTMDensity(weights=[0.5, 0.5], metric="neighbors", dim=1).fit_transform(distances)
+    assert density == pytest.approx(expected)