#!/usr/bin/python3.2
# -*-coding:Utf-8 -*
import math
import numpy as np
import random as rnd
from tfidfStorage import TFIDF_reader
from tfidfStorage import tfidf_cosine_distance as cosdist
from cluster import Cluster
reader = TFIDF_reader("test.vectors")
# Number of tfidf vectors
npoint = reader.doc_nb
# Number of clusters
ncluster = 100
# Initialize centroids so that they are as far from one another as possible
# Centroids indices in tfidf_list
#centroids_idx = [0]
# Distance of each point to the closest cluster
#mindist = np.array([tfidfdct.cosdist(tfidf_list[0]) for tfidfdct in tfidf_list])
#for i in range(1, ncluster) :
# max_idx = np.argmax(mindist)
# centroids_idx.append(max_idx)
# dist = np.array([tfidfdct.cosdist(tfidf_list[max_idx]) for tfidfdct in tfidf_list])
# mindist = np.amin(np.vstack((mindist, dist)), 0)
#clusters = [Cluster(tfidf_list[c_idx]) for c_idx in centroids_idx]
self.__class__(points_1, self.compute_distance, self.get_max_subnodes, self.sample_size),
self.__class__(points_2, self.compute_distance, self.get_max_subnodes, self.sample_size),
]
def threshold_cosine(initial, max, max_count):
def t():
t.count += 1
return float(initial) + float((max - initial) * t.count) / float(max_count)
t.count = 0
return t
if __name__ == "__main__":
reader = TFIDF_reader("test.vectors")
grgpf = GRGPF(
tfidf_cosine_distance,
reader.read_docId,
limit_subnodes=10,
limit_clusters_per_leaf=10,
limit_total_clusters=1000,
limit_total_nodes=200,
k=10,
sample_size=30,
get_next_threshold=threshold_cosine(0.8, 0.99, 10),
)
for i in range(0, reader.doc_nb):
print(i)
if i != 0 and i % 100 == 0: # recalcul des samples tout les 100 points
print("Recomputing samples")
