def test_NNGraph():
Xin = np.arange(90).reshape(30, 3)
dist_types = ['euclidean', 'manhattan', 'max_dist', 'minkowski']
for dist_type in dist_types:
G1 = graphs.NNGraph(Xin, NNtype='knn', dist_type=dist_type)
G2 = graphs.NNGraph(Xin,
use_flann=True,
NNtype='knn',
dist_type=dist_type)
G3 = graphs.NNGraph(Xin, NNtype='radius', dist_type=dist_type)
def test_nngraph(self):
Xin = np.arange(90).reshape(30, 3)
dist_types = ['euclidean', 'manhattan', 'max_dist', 'minkowski']
for dist_type in dist_types:
# Only p-norms with 1<=p<=infinity permitted.
if dist_type != 'minkowski':
graphs.NNGraph(Xin, NNtype='radius', dist_type=dist_type)
graphs.NNGraph(Xin, NNtype='knn', dist_type=dist_type)
# Distance type unsupported in the C bindings,
# use the C++ bindings instead.
if dist_type != 'max_dist':
graphs.NNGraph(Xin, use_flann=True, NNtype='knn',
dist_type=dist_type)
def test_nngraph(self, n_vertices=30):
rs = np.random.RandomState(42)
Xin = rs.normal(size=(n_vertices, 3))
dist_types = ['euclidean', 'manhattan', 'max_dist', 'minkowski']
for dist_type in dist_types:
# Only p-norms with 1<=p<=infinity permitted.
if dist_type != 'minkowski':
graphs.NNGraph(Xin, NNtype='radius', dist_type=dist_type)
graphs.NNGraph(Xin, NNtype='knn', dist_type=dist_type)
# Distance type unsupported in the C bindings,
# use the C++ bindings instead.
if dist_type != 'max_dist':
graphs.NNGraph(Xin, use_flann=True, NNtype='knn',
dist_type=dist_type)
def _make_filter(self, tau=10):
'''
Build graph and heatmap for denoising.
'''
graph = graphs.NNGraph(zip(self.x, self.y), k=self.num_clusters)
graph.estimate_lmax()
# higher tau, spikier signal, less points
fn = filters.Heat(graph, tau=tau)
return fn
def denoise_cluster(self, points, num_cluster, tau=10):
"""
Determine if the cluster after denoising is the same as the original
:param points:
:return: [boolean], false means cluster_id varies, true means cluster_id is preserved
"""
length = len(points)
graph = graphs.NNGraph(points, k=num_cluster)
graph.estimate_lmax()
fn = filters.Heat(graph, tau=tau)
signal = np.empty(num_cluster * length).reshape(
num_cluster, length) # create num_cluster*len(points) matrix
vectors = np.zeros(length * num_cluster).reshape(
length, num_cluster) # create len(points)*num_cluster matrix
# fill the vectors sparse matrix
for i, vec in enumerate(vectors):
vec[self.cluster_list[i]] = 1
vectors = vectors.T
# fill the denoising matrix, find the dominant cluster of each points
for cluster_num, vec in enumerate(vectors):
signal[cluster_num] = fn.analyze(vec)
# see if the dominant cluster after denoising is the same as the original cluster
dominant_cluster = np.argmax(signal, axis=0)
vor_points, vor_clusters = [], []
for index, coor in enumerate(self.points):
if dominant_cluster[index] == int(self.cluster_list[index]):
vor_points.append(coor)
vor_clusters.append(self.cluster_list[index])
self.points = vor_points
self.cluster_list = vor_clusters
from pygsp import graphs
import xarray as xa
import numpy as np
from hyperclass.data.spatial.tile import Tile, Block
import os, math, sys
block_shape = (500, 500)
block_indices = (0, 0)
image_name = "ang20170720t004130_corr_v2p9"
N_neighbors = 8
dm = DataManager(image_name, block_shape=block_shape)
tile: Tile = dm.getTile()
block = tile.getBlock(*block_indices)
data: np.ndarray = block.getPointData().values
graph = graphs.NNGraph(data, 'knn', True, True, True, N_neighbors)
print(".")
def test_NNGraph():
Xin = np.arange(90).reshape(30, 3)
G = graphs.NNGraph(Xin)
needed_attributes_testing(G)
def test_NNGraph():
Xin = np.arange(90).reshape(30, 3)
G = graphs.NNGraph(Xin)