def squared_clustering_errors(inputs, k):
clusterer = KMeans(k)
clusterer.train(inputs)
means = clusterer.means
assignments = map(clusterer.classify, inputs)
return sum(
squared_distance(input, means[cluster])
for input, cluster in zip(inputs, assignments))
def post_kmeantrain(array: str, featurename: str, orderfeature: str):
data = pd.read_json(array)
columnnames = featurename.split(',')
# columnnames = ['DFA', 'violmax', 'maxpeaksqt']
num_examples = data.shape[0]
# Get features.
x_train = data[[iaxis for iaxis in columnnames]].values.reshape(
(num_examples, len(columnnames)))
# print(x_train)
# Set K-Means parameters.
num_clusters = 4 # Number of clusters into which we want to split our training dataset.
max_iterations = 50 # maximum number of training iterations.
# Init K-Means instance.
k_means = KMeans(x_train, num_clusters)
# Train K-Means instance.
(centroids, closest_centroids_ids) = k_means.train(max_iterations)
# print(centroids)
data_frame = pd.DataFrame(centroids,
columns=[iaxis for iaxis in columnnames])
#
dfsort = data_frame.sort_values(by=[orderfeature])
L = [chr(i) for i in range(97, 97 + len(centroids))]
dfsort['L'] = pd.Series(L, index=dfsort.index)
dfreturn = dfsort.set_index('L', drop=True)
# print(dfreturn.to_json(orient="index"))
return dfreturn.to_json(orient="index")
import random from k_means import KMeans from meetup_data import attendees_locations_tuples random.seed(0) clusterer = KMeans(3) clusterer.train(attendees_locations_tuples) print(clusterer.means)
from k_means import KMeans
from matplotlib import image as mat_image
from matplotlib import pyplot
path_to_png_file = "/Users/rileylittlefield/Desktop/squirtle.png"
squirtle_image = mat_image.imread(path_to_png_file)
squirtle_pixels = [pixel for row in squirtle_image for pixel in row]
clusterer = KMeans(5)
clusterer.train(squirtle_pixels)
def recolor(pixel):
cluster = clusterer.classify(pixel)
return clusterer.means[cluster]
new_squirtle_image = [[recolor(pixel) for pixel in row]
for row in squirtle_image]
pyplot.imshow(new_squirtle_image)
pyplot.axis('off')
pyplot.show()
#!/usr/bin/env python3
import sys
sys.path.append('code')
import numpy as np
from k_means import KMeans
# Pokemon heigh/weight
data = np.array([[0.4, 6.0], # Pikachu
[0.7, 6.9], # Bulbasaur
[0.6, 8.5], # Charmander
[0.5, 9.0], # Squirtle
[1.2, 36.0], # Slowpoke
[1.6, 78.5], # Slowbro
[1.1, 90.0], # Seel
[1.7, 120.0],# Dewgong
[2.2, 210.0],# Dragonite
[1.7, 55.4], # Articuno
[1.6, 52.6], # Zapdos
[2.0, 60.0]] # Moltres
)
if __name__ == "__main__":
k_means = KMeans(2)
k_means.train(data)
k_means.report()
plt.legend()
plt.subplot(1, 2, 2)
plt.scatter(data[x_axis][:], data[y_axis][:])
plt.title('label unknown')
plt.show()
num_examples = data.shape[0]
x_train = data[[x_axis, y_axis]].values.reshape(num_examples, 2)
#指定好训练所需的参数
num_clusters = 3
max_iteritions = 50
k_means = KMeans(x_train, num_clusters)
centroids, closest_centroids_ids = k_means.train(max_iteritions)
# 对比结果
plt.figure(figsize=(12, 5))
plt.subplot(1, 2, 1)
for iris_type in iris_types:
plt.scatter(data[x_axis][data['class'] == iris_type],
data[y_axis][data['class'] == iris_type],
label=iris_type)
plt.title('label known')
plt.legend()
plt.subplot(1, 2, 2)
for centroid_id, centroid in enumerate(centroids):
current_examples_index = (closest_centroids_ids == centroid_id).flatten()
plt.scatter(data[x_axis][current_examples_index],
data[y_axis][current_examples_index],
from k_means import KMeans
import numpy
import pickle
dic = pickle.load(open("dat2.dat", "rb"))
#pickle.dump(dic, open("dat2.dat","wb"), protocol=2)
lst = []
for each in dic["vectors"]:
temp = []
for lol in each["vectors"]:
if len(lol) != 300:
pass
else:
temp.append(lol)
s = numpy.sum(temp, axis=0)
try:
if len(s) == 300:
lst.append(s.tolist())
except:
pass
word_vectors = lst
num_clusters = 2
k_means = KMeans(num_clusters, word_vectors)
k_means.train()
print(k_means.get_cluster(lst[0]))
def run_sfs(self, num_clusters=2):
chosen_features = list(
) #list of column numbers corresponding to chosen features
chosen_data_set = pd.DataFrame()
self.base_performance = -2
current_performance = -1
best_performance = -1
best_features = -1
#best_model = TestModel(self.data, clusters) #used only for testing
best_model = KMeans(self.data, num_clusters)
self.chosen_model = best_model
while (1):
num_chosen_features = len(
chosen_features) #use as next col index of data
#print('number of chosen features:', num_chosen_features)
#print('Iterate over all features')
#print('------------------------')
for column in self.data:
#Account for (skip) features already been chosen as best
if column in chosen_features:
#print('Already selected this column', column, ' Skippin')
continue
#Select a feature (column)
chosen_features.append(column)
#Get the feature vector (column)
feature_vector = self.data[column]
#Get the data set of ALL chosen
chosen_data_set[num_chosen_features] = feature_vector
#model = TestModel(chosen_data_set, 2) #TESTING ONLY
model = KMeans(chosen_data_set, num_clusters)
model.train()
current_performance = model.evaluate()
print('for features ', chosen_features, 'best perf', best_performance, \
' vs. current_perf', current_performance)
#print('chosen_data_set')
#print(chosen_data_set)
if current_performance > best_performance:
best_performance = current_performance
best_model = model
best_feature = column
best_data = pd.DataFrame(chosen_data_set)
#Remove the data & chosen feature & get next feature/data
column_to_drop = len(chosen_features) - 1
chosen_data_set.drop(chosen_data_set.columns[column_to_drop],
axis=1,
inplace=True)
chosen_features.pop()
#print('------------------------')
#print('best perf', best_performance, ' vs. base_performance', base_performance)
if best_performance > self.base_performance:
self.base_performance = best_performance
chosen_features.append(best_feature)
chosen_data_set = best_data
self.chosen_model = best_model
#print('base performance now best perf', base_performance)
#print('chosen feature column', best_feature)
#print(chosen_data_set)
else:
break
#print('final best performance', base_performance)
#print('chosen features')
#print(chosen_features)
#print(chosen_data_set)
return chosen_features, chosen_data_set
