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)
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)
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))
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))
