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idw_knn.py
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# -*- coding: utf-8 -*-
# Copyright (C) 2016 Paul Brodersen <[email protected]>
# Author: Paul Brodersen <[email protected]>
# https://github.com/paulbrodersen/inverse_distance_weighting
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 3
# of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
Inverse distance weighting (IDW)
--------------------------------
Compute the score of query points based on the scores of their k-nearest neighbours,
weighted by the inverse of their distances.
Example:
--------
# import idw
# 'train'
idw_tree = idw.tree(X1, z1)
# 'test'
spacing = np.linspace(-5., 5., 100)
X2 = np.meshgrid(spacing, spacing)
grid_shape = X2[0].shape
X2 = np.reshape(X2, (2, -1)).T
z2 = idw_tree(X2)
For a more complete example see demo().
"""
import numpy as np
from scipy.spatial import cKDTree
class tree(object):
"""
Compute the score of query points based on the scores of their k-nearest neighbours,
weighted by the inverse of their distances.
@reference:
https://en.wikipedia.org/wiki/Inverse_distance_weighting
Arguments:
----------
X: (N, d) ndarray
Coordinates of N sample points in a d-dimensional space.
z: (N,) ndarray
Corresponding scores.
leafsize: int (default 10)
Leafsize of KD-tree data structure;
should be less than 20.
Returns:
--------
tree instance: object
Example:
--------
# 'train'
idw_tree = tree(X1, z1)
# 'test'
spacing = np.linspace(-5., 5., 100)
X2 = np.meshgrid(spacing, spacing)
X2 = np.reshape(X2, (2, -1)).T
z2 = idw_tree(X2)
See also:
---------
demo()
"""
def __init__(self, X=None, z=None, leafsize=10):
if not X is None:
self.tree = cKDTree(X, leafsize=leafsize )
if not z is None:
self.z = np.array(z)
def fit(self, X=None, z=None, leafsize=10):
"""
Instantiate KDtree for fast query of k-nearest neighbour distances.
Arguments:
----------
X: (N, d) ndarray
Coordinates of N sample points in a d-dimensional space.
z: (N,) ndarray
Corresponding scores.
leafsize: int (default 10)
Leafsize of KD-tree data structure;
should be less than 20.
Returns:
--------
idw_tree instance: object
Notes:
-------
Wrapper around __init__().
"""
return self.__init__(X, z, leafsize)
def __call__(self, X, k=6, eps=1e-6, p=2, regularize_by=1e-9):
"""
Compute the score of query points based on the scores of their k-nearest neighbours,
weighted by the inverse of their distances.
Arguments:
----------
X: (N, d) ndarray
Coordinates of N query points in a d-dimensional space.
k: int (default 6)
Number of nearest neighbours to use.
p: int or inf
Which Minkowski p-norm to use.
1 is the sum-of-absolute-values "Manhattan" distance
2 is the usual Euclidean distance
infinity is the maximum-coordinate-difference distance
eps: float (default 1e-6)
Return approximate nearest neighbors; the k-th returned value
is guaranteed to be no further than (1+eps) times the
distance to the real k-th nearest neighbor.
regularise_by: float (default 1e-9)
Regularise distances to prevent division by zero
for sample points with the same location as query points.
Returns:
--------
z: (N,) ndarray
Corresponding scores.
"""
self.distances, self.idx = self.tree.query(X, k, eps=eps, p=p)
self.distances += regularize_by
weights = self.z[self.idx.ravel()].reshape(self.idx.shape)
mw = np.sum(weights/self.distances, axis=1) / np.sum(1./self.distances, axis=1)
return mw
def transform(self, X, k=6, p=2, eps=1e-6, regularize_by=1e-9):
"""
Compute the score of query points based on the scores of their k-nearest neighbours,
weighted by the inverse of their distances.
Arguments:
----------
X: (N, d) ndarray
Coordinates of N query points in a d-dimensional space.
k: int (default 6)
Number of nearest neighbours to use.
p: int or inf
Which Minkowski p-norm to use.
1 is the sum-of-absolute-values "Manhattan" distance
2 is the usual Euclidean distance
infinity is the maximum-coordinate-difference distance
eps: float (default 1e-6)
Return approximate nearest neighbors; the k-th returned value
is guaranteed to be no further than (1+eps) times the
distance to the real k-th nearest neighbor.
regularise_by: float (default 1e-9)
Regularise distances to prevent division by zero
for sample points with the same location as query points.
Returns:
--------
z: (N,) ndarray
Corresponding scores.
Notes:
------
Wrapper around __call__().
"""
return self.__call__(X, k, eps, p, regularize_by)
def demo():
import matplotlib.pyplot as plt
# create sample points with structured scores
X1 = 10 * np.random.rand(1000, 2) -5
def func(x, y):
return np.sin(x**2 + y**2) / (x**2 + y**2)
z1 = func(X1[:,0], X1[:,1])
# 'train'
idw_tree = tree(X1, z1)
# 'test'
spacing = np.linspace(-5., 5., 100)
X2 = np.meshgrid(spacing, spacing)
grid_shape = X2[0].shape
X2 = np.reshape(X2, (2, -1)).T
z2 = idw_tree(X2)
# plot
fig, (ax1, ax2, ax3) = plt.subplots(1,3, sharex=True, sharey=True, figsize=(10,3))
ax1.contourf(spacing, spacing, func(*np.meshgrid(spacing, spacing)))
ax1.set_title('Ground truth')
ax2.scatter(X1[:,0], X1[:,1], c=z1, linewidths=0)
ax2.set_title('Samples')
ax3.contourf(spacing, spacing, z2.reshape(grid_shape))
ax3.set_title('Reconstruction')
plt.show()
return