def cluster(self):
l_method = agglomerative_l_method(self.X)
suggest_n = len(l_method.cluster_centers_)
agg = AgglomerativeClustering(suggest_n)
agg.fit(np.array(self.X, copy=True))
# agg.fit(self.X)
# agg_labels = agg.labels_
# l_method_labels = l_method.labels_
#
# print('agg_labels:', agg_labels)
# print('l_method_labels:', l_method_labels)
# first tier clustering, using agglomerative clustering
self.clustering_model = DividableClustering()
self.clustering_model.fit(self.X, l_method.labels_)
def main(argv):
path = argv[1]
samples = Data(path).create_samples()
single_link = SingleLink()
print("single link:")
agglomerate = AgglomerativeClustering(single_link, samples)
agglomerate.run(7)
print("")
complete_link = CompleteLink()
print("complete link:")
agglomerate = AgglomerativeClustering(complete_link, samples)
agglomerate.run(7)
def main(argv):
path = argv[1]
genes_data = Data(path)
sample_list = genes_data.create_samples()
single_agro_clustering = AgglomerativeClustering(SingleLink, sample_list)
complete_agro_clustering = AgglomerativeClustering(CompleteLink,
sample_list)
single_final_clusters = single_agro_clustering.run(int(argv[3]))
Complete_final_clusters = complete_agro_clustering.run(int(argv[3]))
missions_to_print = argv[2].split(", ")
general_printer(missions_to_print, single_final_clusters,
Complete_final_clusters)
import pandas as pd
import sklearn.cluster as sklearn_cluster
from sklearn import datasets
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from agglomerative_clustering import AgglomerativeClustering
iris = datasets.load_iris()
X = iris.data
y = iris.target
n_clusters = len(iris.target_names)
print("\n===========================\n")
print("Agglomerative Clustering (Single) from Scratch")
y_predict = AgglomerativeClustering(pd.DataFrame(X), n_clusters,
'single').fit_predict()
print(y_predict)
print('Confusion Matrix :', confusion_matrix(y, y_predict))
print('Accuracy Score :', accuracy_score(y, y_predict))
print("\n===========================\n")
print("Agglomerative Clustering (Single) SKLearn")
y_predict = sklearn_cluster.AgglomerativeClustering(
linkage='single').fit_predict(X)
print(y_predict)
print('Confusion Matrix :', confusion_matrix(y, y_predict))
print('Accuracy Score :', accuracy_score(y, y_predict))
from agglomerative_clustering import AgglomerativeClusteringMaxMergeDist, AgglomerativeClustering
from dataset import *
dataset = get_iris()
print("dataset size:", len(dataset.X))
#
# agg = AgglomerativeClusteringMaxMergeDist()
# centroids, cluster_member_cnt = agg.fit(dataset.X, 0.2)
#
# print('grouped size:', len(centroids))
agg = AgglomerativeClustering(3)
agg.fit(dataset.X)
predict_X = agg.predict(dataset.X)
print("predict_X:", predict_X)
from dividable_clustering import DividableClustering from agglomerative_clustering import AgglomerativeClustering from sklearn.cluster import KMeans from dataset import * from sklearn.neighbors import BallTree dataset = get_iris() agg = AgglomerativeClustering(3) agg.fit(dataset.X) model = DividableClustering() model.fit(dataset.X, agg.labels_) print(len(model.X_by_label[0])) print(len(model.X_by_label[1])) print(len(model.X_by_label[2])) kmeans = KMeans(3) kmeans.fit(model.get_X(0)) model.split(0, kmeans.labels_) print(len(model.X_by_label[3])) print(len(model.X_by_label[4])) print(len(model.X_by_label[5])) print(model.X_by_label.keys()) model.relabel()
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.metrics.cluster import normalized_mutual_info_score
from utils import *
from datasets import *
from classifiers import *
from metrics import *
from agglomerative_clustering import AgglomerativeClustering
from dbscan import DBSCAN
X, y = read_dataset(dataset='Iris')
print("--- AgglomerativeClustering ---")
model = AgglomerativeClustering(n_clusters=3,
verbose=False,
linkage='complete',
distance_metric='l1')
cluster_pred = model.fit_predict(X)
print("adjusted_rand_score", metrics.adjusted_rand_score(y, cluster_pred))
print(" normalized_mutual_info_score",
normalized_mutual_info_score(y, cluster_pred))
print("--- DBSCAN ---")
cluster_pred = DBSCAN(eps=1, MinPts=5).fit_predict(X)
print("adjusted_rand_score", metrics.adjusted_rand_score(y, cluster_pred))
print(" normalized_mutual_info_score",
normalized_mutual_info_score(y, cluster_pred))