def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert_equal(centers.shape, (n_clusters, n_features))
labels = labels
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignements are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(inertia, 0.0)
# check warning when centers are passed
with warnings.catch_warnings(record=True) as w:
k_means(X, n_clusters=n_clusters, init=centers)
assert_equal(len(w), 1)
# to many clusters desired
assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1)
def kmeans_analysis(G):
block = nx.get_node_attributes(G,'block').values()
xA, xL = get_embedding(G,2)
cA,kmA,_ = k_means(xA,2)
cB,kmL,_ = k_means(xL,2)
# plt.subplot(221); plt.scatter(xA[:,0],xA[:,1],c=block)
# plt.subplot(222); plt.scatter(xA[:,0],xA[:,1],c=kmA)
# plt.subplot(223); plt.scatter(xL[:,0],xL[:,1],c=block)
# plt.subplot(224); plt.scatter(xL[:,0],xL[:,1],c=kmL)
ax = plt.subplot(121); plt.scatter(xA[:,0],xA[:,1],c=block,marker='x')
ax.set_aspect('equal','datalim')
lim = plt.axis()
a = cA[0,:]-cA[1,:]
a = np.array([1, -a[0]/a[1]])
b = np.mean(cA,axis=0)
x = np.array([b+a,b-a])
plt.plot(x[:,0],x[:,1],'k--',linewidth=1)
plt.axis(lim)
ax = plt.subplot(122); plt.scatter(xL[:,0],xL[:,1],c=block,marker='x')
ax.set_aspect('equal','datalim')
lim = plt.axis()
a = cB[0,:]-cB[1,:]
a = np.array([1, -a[0]/a[1]])
b = np.mean(cB,axis=0)
x = np.array([b+a,b-a])
plt.plot(x[:,0],x[:,1],'k--',linewidth=1)
plt.axis(lim)
compare_results(block,kmA,kmL)
_,kmA,_ = k_means(xA,5)
_,kmL,_ = k_means(xL,5)
print "ALL FIVE"
num_diff = vn.num_diff_w_perms(block, kmA)
ari = adjusted_rand_score(block,kmA)
print "Adjacency: num error="+repr(num_diff)+" ari="+repr(ari)
num_diff = vn.num_diff_w_perms(block, kmL)
ari = adjusted_rand_score(block,kmL)
print "Laplacian: num error="+repr(num_diff)+" ari="+repr(ari)
def getCenteroidsByGapStats(dataToCluster, maxCluster):
# plot data
xminData = np.min(dataToCluster[:,0])
xmaxData = np.max(dataToCluster[:,0])
yminData = np.min(dataToCluster[:,1])
ymaxData = np.max(dataToCluster[:,1])
numOfClusterRuns = maxCluster - 1
sumSqMetricSave = np.zeros((1, numOfClusterRuns))
wksMetricSave = np.zeros((1, numOfClusterRuns))
wkbsMetricSave = np.zeros((1, numOfClusterRuns))
kMetricSave = np.zeros((1, numOfClusterRuns), dtype=np.int32)
skMetricSave = np.zeros((1, numOfClusterRuns))
centeroidSave = []
labelSave = []
for clusterRun in xrange(1, maxCluster):
centroids, labels, inertia = k_means(dataToCluster, n_clusters = clusterRun)
centeroidSave.append(centroids)
labelSave.append(labels)
kMetricSave[0, clusterRun-1] = clusterRun
# calculate gap stattistics for selecting the number of clusters
tempVar = calculateWk(centroids, labels, dataToCluster)
sumSqMetricSave[0, clusterRun-1] = tempVar
wksMetricSave[0, clusterRun-1] = np.log(tempVar)
# ref data set
bRef = 10
BWkbs = np.zeros((1, bRef))
for iRun in xrange(bRef):
refData = np.zeros_like(dataToCluster)
for dataRun in xrange(dataToCluster.shape[0]):
refData[dataRun,:] = np.array([np.random.uniform(xminData ,xmaxData), np.random.uniform(yminData, ymaxData)])
centroidsRef, labelsRef, inertiaRef = k_means(refData, n_clusters = clusterRun)
BWkbs[0, iRun] = np.log(calculateWk(centroidsRef, labelsRef, refData))
wkbsMetricSave[0, clusterRun-1] = np.sum(BWkbs)/float(bRef)
skMetricSave[0, clusterRun-1] = np.sqrt(np.sum((BWkbs - wkbsMetricSave[0, clusterRun-1])**2)/float(bRef))
skMetricSave = skMetricSave*np.sqrt(1 + 1/float(bRef))
# gap statistics
gap = (wkbsMetricSave - wksMetricSave)
gap= gap.reshape(1, -1)
finalMetric = np.zeros((1, numOfClusterRuns))
for iRun in xrange(1, maxCluster-1):
# gapofk-gapofk+1-sk
finalMetric[0, iRun-1] = gap[0, iRun-1] - (gap[0, iRun] - skMetricSave[0, iRun])
indeNonZero = np.where(finalMetric>0)[1]
selectIndex = np.min(indeNonZero)
# final clustering pics
selectCenteroids = np.array(centeroidSave[selectIndex])
selectLabels = np.array(labelSave[selectIndex])
return selectCenteroids
def test_faith():
"""The 1st part of project3"""
data_1 = xlrd.open_workbook(r'G:\pyproj\EE511Proj3\oldfaithful.xlsx') #Change the directory to file directory
table = data_1.sheet_by_name(u'Sheet1')
x = table.col_values(0)
y = table.col_values(1)
c = []
for i in range(0,len(x)):
c.append(table.row_values(i))
print(c)
[centroid,label,inertia] = cluster.k_means(c,2)
print(centroid)
print(label)
print(inertia)
for a in range(0,len(label)):
plt.scatter(x[a],y[a],c = 'b')
plt.xlabel('eruptions')
plt.ylabel('waiting')
plt.title('Raw Data')
plt.show()
for j in range(0,len(label)):
if label[j] == 1:
plt.scatter(x[j],y[j],marker = '.',c = 'r')
elif label[j] == 0:
plt.scatter(x[j],y[j],marker = '*',c = 'b')
plt.xlabel('eruptions')
plt.ylabel('waiting')
plt.title('Clustering of Data')
plt.show()
plt.clf()
def check_cluster(cluster):
n = len(cluster)
if n < 2:
return True, []
# Run k_means on two centers
children, labels, _ = k_means(cluster, 2)
# Let v = c1 - c2 be a d-dimensional vector that connects the two centers. This is the direction that k-means
# believes to be important for clustering.
v = children[1]-children[0]
# Then project X onto v: x'i = hxi, vi/||v||2. X0 is a 1-dimensional
# representation of the data projected onto v.
x_prime = [np.dot(point, v) for point in cluster]
# Transform X0 so that it has mean 0 and variance 1.
x_prime = zscore(x_prime)
# Let zi = F(x0(i)). If A2*(Z) is in the range of non-critical values at confidence level alpha, then accept H0,
# keep the original center, and discard {c1, c2}. Otherwise, reject H0 and keep {c1, c2} in place of the original
# center.
a2, critical, sig = anderson(x_prime)
a2 *= (1+4.0/n-25.0/(n**2))
return a2 < critical[0], children
def k_means_classifier(image):
n_clusters = 8
# blur and take local maxima
blur_image = gaussian(image, sigma=8)
blur_image = ndi.maximum_filter(blur_image, size=3)
# get texture features
feats = local_binary_pattern(blur_image, P=40, R=5, method="uniform")
feats_r = feats.reshape(-1, 1)
# cluster the texture features
km = k_means(n_clusters=n_clusters, batch_size=500)
clus = km.fit(feats_r)
# copy relevant attributes
labels = clus.labels_
clusters = clus.cluster_centers_
# reshape label arrays
labels = labels.reshape(blur_image.shape[0], blur_image.shape[1])
# segment shadow
img = blur_image.ravel()
shadow_seg = img.copy()
for i in range(0, n_clusters):
# set up array of pixel indices matching cluster
mask = np.nonzero((labels.ravel() == i) == True)[0]
if len(mask) > 0:
thresh = threshold_otsu(img[mask])
shadow_seg[mask] = shadow_seg[mask] < thresh
shadow_seg = shadow_seg.reshape(*image.shape)
return shadow_seg
def cpie_mech_dy(turn):
km = k_means(np.array([turn]).T, 2, random_state=1234)[0].flatten()
km = np.sort(np.append(km, km.mean()))
mech = np.zeros_like(turn)
mech[((turn > km[0]) & (turn <= km[1]))] = 1
mech[(turn > km[2])] = 1
return mech
def feature_clust(f_pool, f_train, n_clust, method='unsupervised-spectral'):
N_pool = len(f_pool)
data_f_pool = list(f_pool)
data_f_train = list(f_train)
data_f_pool.extend(data_f_train)
if method == 'unsupervised-ds-svm':
labels = ds_svm_clustering(data_f_pool,
n_clust=n_clust,
eta=4,
ds_ratio=0.25,
plot=False,
metric='euclidean')
elif method == 'unsupervised-spectral':
spectral = cl.SpectralClustering(n_clusters=n_clust,
eigen_solver='arpack',
affinity="nearest_neighbors",
n_jobs=6)
spectral.fit(data_f_pool)
labels = spectral.labels_
elif method == 'unsupervised-kmeans':
clusters = cl.k_means(data_f_pool, n_clust) #Kmeans Clustering
labels = clusters[1]
elif method == 'unsupervised-kmedoids':
_, labels = k_medoids_selection(data_f_pool, n_clust)
else:
raise ValueError('Invalid clustering method!')
clust_pool = labels[0:N_pool]
clust_train = labels[N_pool:]
return clust_pool, clust_train
def regularized_spectral_clustering(adj_matrix, tau, n_clusters, algo='scan'):
"""
:param adj_matrix: adjacency matrix representation of graph where [m][n] >0 if there is edge and [m][n] = weight
:param n_clusters: cluster partitioning constant
:param algo: the clustering separation algorithm, possible value kmeans++ or scan
:return: labels, number of clustering iterations needed, smallest set of cluster found, execution time
"""
start = timer()
regularized_laplacian = regularized_laplacian_matrix(adj_matrix, tau)
eigen_values, eigen_vectors = eigen_solver(regularized_laplacian, n_clusters=n_clusters)
if algo == 'kmeans++':
_, labels, _, num_iterations = k_means(eigen_vectors,
n_clusters=n_clusters,
return_n_iter=True)
else:
if n_clusters == 2: # cluster based on sign
second_eigen_vector_index = np.argsort(eigen_values)[1]
second_eigen_vector = eigen_vectors.T[second_eigen_vector_index]
labels = [0 if val <= 0 else 1 for val in second_eigen_vector] # use only the second eigenvector
num_iterations = 1
else: # bisecting it into k-ways, use all eigenvectors
labels = discretize(eigen_vectors)
num_iterations = 20 # assume worst case scenario that it tooks 20 restarts
end = timer()
execution_time = end - start
smallest_cluster_size = min(np.sum(labels), abs(np.sum(labels) - len(labels)))
return labels, num_iterations, smallest_cluster_size, execution_time
def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X,
n_clusters=n_clusters,
sample_weight=None,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert centers.shape == (n_clusters, n_features)
assert np.unique(labels).shape[0] == n_clusters
# check that the labels assignment are perfect (up to a permutation)
assert v_measure_score(true_labels, labels) == 1.0
assert inertia > 0.0
# check warning when centers are passed
assert_warns(RuntimeWarning,
k_means,
X,
n_clusters=n_clusters,
sample_weight=None,
init=centers)
def spectral_clustering(road_map=None, a=None, use_ncut=False, num_clusters=2):
print("Building adjacency matrix")
if(a==None):
a = build_adjacency_matrix(road_map)
print("Computing laplacian")
l = build_laplacian(a, normalize=use_ncut)
print("Spectral embedding")
#e_vals, e_vects = eigsh(l, k=num_clusters, which='SM', tol=0.01, sigma=2.01)
X = np.random.rand(l.shape[0], num_clusters+1)
e_vals, e_vects = lobpcg(l, X, tol=1e-15,
largest=False, maxiter=2000)
embedded_data = e_vects[:,1:]
print e_vals
print("Clustering")
centroid, label, intertia = k_means(embedded_data, num_clusters)
for i in xrange(len(label)):
road_map.nodes[i].region_id = label[i]
def clusters(n = 100):
from sklearn.cluster import k_means
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from matplotlib.patches import PathPatch
from mapFuncts import USbasemap
from matplotlib import cm, colors, colorbar
fig = plt.figure()
ax = fig.add_subplot(111)
fig, ax, m = USbasemap(fig = fig, ax = ax)
alldivDF, _, _, _, _ = seasonal_setup(season = 'MAM')
centroid, label, inertia = k_means(alldivDF.T, n_clusters = n, init = 'k-means++')
names = alldivDF.columns
codes = reverseStates(importStates())
cmap = cm.Accent
norm = colors.Normalize(vmin = 0, vmax = n, clip = True)
mapper = cm.ScalarMappable(norm = norm, cmap = cmap)
for cluster in range(n):
idx = label==cluster
patches = []
color = mapper.to_rgba(cluster)
for name in names[idx]:
divcode = codes[name[:-3]]+name[-2:]
print name, divcode
for info, shape in zip(m.divs_info, m.divs):
if info['CLIMDIV']==int(divcode):
patches.append(Polygon(np.array(shape),True))
ax.add_collection(PatchCollection(patches, facecolor = color, edgecolor='k', linewidths=1., zorder=2))
return fig, ax
def fit(
self,
data: List[Iterator[float]],
find_n: bool = False
) -> Dict[str, Union[List[int], Union[float, None]]]:
"""Cluster the input data into n clusters.
Args:
data: A list of vectors.
find_n: If True, don't use self.n_cluster but find n using
elbow analysis instead
Return:
A list of integers as class labels. The order of the list
corresponds to the order of the input data.
"""
if find_n:
self.n_clusters = 5 # self._get_n()
if self.clus_type == 'kmeans':
self.cluster = k_means(n_clusters=self.n_clusters)
elif self.clus_type == 'sphericalkmeans':
self.cluster = SphericalKMeans(n_clusters=self.n_clusters)
elif self.clus_type == 'agglomerative':
self.cluster = AgglomerativeClustering(n_clusters=self.n_clusters,
affinity=self.affinity,
linkage=self.linkage)
self.cluster.fit(data)
self._calc_density()
return {'labels': self.cluster.labels_, 'density': self.compactness}
def sweep_clusters(args):
data = joblib.load(args.model_file)
projected = data[PROJECTION_KEY]
print "Model type", data[MODEL_TYPE_KEY]
if not os.path.exists(args.figures_dir):
os.makedirs(args.figures_dir)
inertia_values = []
for k in args.n_clusters:
print "Clustering with %s states" % k
_, _, inertia = k_means(projected[:, args.dimensions],
k,
n_jobs=-2)
inertia_values.append(inertia)
plt.plot(args.n_clusters,
inertia_values,
"k.-")
plt.xlabel("Number of Clusters", fontsize=16)
plt.ylabel("Inertia", fontsize=16)
fig_flname = os.path.join(args.figures_dir,
"cluster_inertia")
for dim in args.dimensions:
fig_flname += "_%s" % dim
fig_flname += ".png"
plt.savefig(fig_flname,
DPI=300)
def spectral_clustering(affinity,
n_clusters=8,
n_components=None,
eigen_solver=None,
random_state=None,
n_init=10,
eigen_tol=0.0,
assign_labels='kmeans'):
if assign_labels not in ('kmeans', 'discretize',
'AgglomerativeClustering'):
raise ValueError("The 'assign_labels' parameter should be "
"'kmeans' or 'discretize', but '%s' was given" %
assign_labels)
random_state = check_random_state(random_state)
n_components = n_clusters if n_components is None else n_components
maps = spectral_embedding(affinity,
n_components=n_components,
eigen_solver=eigen_solver,
random_state=random_state,
eigen_tol=eigen_tol,
drop_first=False)
if assign_labels == 'kmeans':
_, labels, _ = k_means(maps, n_clusters)
else:
labels = discretize(maps, random_state=random_state)
return labels
def clust(X,vectorfile,clust_file,clust_number):
fid2fname = {}
for line in open(vectorfile) :
line = line.strip().split('\t')
fid2fname.setdefault(int(line[0]), line[1:])
# Force the solver to be arpack, since amg is numerically
# unstable on this example
# labels = spectral_clustering(graph, n_clusters=160, eigen_solver='arpack')
a, labels,inertia = cluster.k_means(X,n_clusters=clust_number)
print ("inertia=",inertia)
C=np.column_stack((X,labels))
easy_data=open(clust_file, 'w')
for pnt1 in C :
strx=""
for charest in pnt1:
strx+=str(int(charest))+"\t"
print >> easy_data, strx
easy_data.close()
return inertia
def kmeans(data, depth):
original = data
data = data[data.PRES < depth]
if data[data.DATA_MODE != "D"].shape[0] > 0 and data[data.DATA_MODE ==
"D"].shape[0] > 0:
# Get mid-range of DMQC and RTQC data
rm_d = (data[data.DATA_MODE == "D"].PSAL.max() +
data[data.DATA_MODE == "D"].PSAL.min()) / 2
rm_r = (data[data.DATA_MODE != "D"].PSAL.max() +
data[data.DATA_MODE != "D"].PSAL.min()) / 2
# K means algorithm
init = array([[rm_d], [rm_r]])
centroid, labels, loss = k_means(data[["PSAL"]],
2,
init=init,
n_init=1)
else:
return (False, original, [], [])
data.insert(data.shape[1], "LABEL", labels)
# Get data from the second group
gruped = data[data.LABEL == 1].groupby(["PLATFORM_NUMBER", "CYCLE_NUMBER"
]).size().reset_index()
platform_numbers = gruped.PLATFORM_NUMBER.unique().tolist()
temp_data = original[
(original.PLATFORM_NUMBER.isin(gruped.PLATFORM_NUMBER))
& (original.CYCLE_NUMBER.isin(gruped.CYCLE_NUMBER))]
# If the second group has DMQC data, the flag is False
flag = False if temp_data[temp_data.DATA_MODE ==
"D"].shape[0] > 0 else True
# Get data from the first group
temp_data = original[~(
(original.PLATFORM_NUMBER.isin(gruped.PLATFORM_NUMBER)) &
(original.CYCLE_NUMBER.isin(gruped.CYCLE_NUMBER)))]
return (flag, original, temp_data, platform_numbers)
def kmeans_cluster(self, data, n_clusters, maximum):
"""
Calculate K-means and plot
"""
# Make clusters
from sklearn.cluster import k_means
import numpy as np
init = np.array([[1.25, 0.28], [4.29, 0.71], [0.99, 1.15],
[6.5, 1.011]])
centroids, labels, sse = k_means(data,
n_clusters=n_clusters,
init=init,
n_init=100)
# Plot
from matplotlib import pyplot as plt
plt.figure(figsize=(10, 7))
# Define color map
#cmap = 'jet'
cmap = self.color_map('kmeans')
# De-normalize before plot
plt.scatter(data[:, 0] * maximum, data[:, 1], c=labels, cmap=cmap)
plt.title('Clusters - k-means')
plt.xlabel('var0')
plt.ylabel('var1')
plt.savefig('clusters_kmeans.png')
plt.close()
return ()
def draw_kmeans(item):
print 'dataset:', items[item]
train_data = tools.data_pre(item)
[centroid, label, inertia] = cluster.k_means(train_data, cluster_k)
#sava data into the csv files
root_path = root_dir + 'data\kmeans' + os.sep + items[item] + os.sep
print 'result path:', root_path
if not os.path.isdir(root_path):
os.makedirs(root_path)
time.sleep(2)
label_path = root_path + items[item] + '_label.csv'
count_path = root_path + items[item] + '_count.csv'
cent_path = root_path + items[item] + '_cent.csv'
np.savetxt(label_path, label, delimiter=',')
pd.value_counts(label).T.to_csv(count_path) # value_counts()计算非空值计数的直方图。
df = pd.DataFrame(centroid) # T 转置; to_csv()将数据写入csv文件
df.to_csv(cent_path, float_format='%.5f')
index = 1
for i in centroid:
plt.figure(index)
title_name = items[item] + '_kmeans_' + str(index) #标题名称
plt.title(title_name)
pd.Series(i).plot() #画图
path = root_dir + '\data\kmeans\img' + items[item] + '_kmeans_' + str(
index) + '.png'
plt.savefig(path) #保存
index += 1
def sklearn_spectral_clustering(adj_matrix, n_clusters):
"""
:param adj_matrix: adjacency matrix representation of graph where [m][n] >0 if there is edge and [m][n] = weight
:param n_clusters: cluster partitioning constant
:return: labels, number of clustering iterations needed, smallest set of cluster found, execution time
"""
start = timer()
connectivity = kneighbors_graph(adj_matrix, n_neighbors=10,
include_self=True)
affinity_matrix_ = 0.5 * (connectivity + connectivity.T)
eigen_vectors = spectral_embedding(affinity_matrix_,
n_components=n_clusters,
eigen_solver='arpack',
eigen_tol=0.0,
norm_laplacian=True,
drop_first=False)
_, labels, _, num_iterations = k_means(eigen_vectors,
n_clusters=n_clusters,
return_n_iter=True)
end = timer()
execution_time = end - start
smallest_cluster_size = min(np.sum(labels), abs(np.sum(labels) - labels.size))
return labels, num_iterations, smallest_cluster_size, execution_time
def fun(train, test):
granular_balls = GBList(train, train) # Build granular balls
granular_balls.init_granular_balls() # Initialize granular balls, divide granular balls according to purity
ball_list = granular_balls.granular_balls
Ball_list = funtion(ball_list) # Continue to divide the granular balls with overlapping boundaries
while True:
init_center = []
Ball_num1 = len(Ball_list) # Count the number of granular balls
for i in range(len(Ball_list)):
init_center.append(Ball_list[i].center)
ClusterLists = k_means(X=train[:, 1:-1], init=np.array(init_center), n_clusters=len(Ball_list))
data_label = ClusterLists[1]
ball_list = []
for i in set(data_label):
Cluster_data = train[data_label == i, :]
ball_list.append(GranularBall(Cluster_data))
Ball_list = funtion(ball_list)
Ball_num2 = len(Ball_list) # Number of granular balls after statistical division
if Ball_num1 == Ball_num2: # Stop if the number of granular balls no longer changes
break
# plot_gb(Ball_list) # Visualize two-dimensional granular balls (using data.csv data set)
ball_num = len(Ball_list) # Number of granular balls generated
time1 = time.time()
count_num = nearest_knn(Ball_list, test) # Test nearest neighbor accuracy
time2 = time.time()
test_time = time2 - time1 # Statistical classification time
return count_num, ball_num, test_time
def testGeneratedData():
X, y = make_blobs(n_samples=1000, centers=np.array([[-2.5, 0], [2.5, 0], [0, 2.5], [0, -2.5]]))
n_clusters = np.unique(y).shape[0]
n_instances = X.shape[0]
# Delete some seeds:
yMod = np.copy(y)
for i in range(n_instances):
if np.random.rand() > 0.5:
yMod[i] = -1 # Means no class
sm = ConstrainedKMeans(n_clusters=n_clusters, max_iter=2000, verbose=1)
sm.fit(X, yMod)
predictedLabels = sm.predict(X)
trueLabels = y
adjustedRandScore = metrics.adjusted_rand_score(trueLabels, predictedLabels)
print('Constrained KMeans adjusted rand score: %s' % (adjustedRandScore))
(centers, predictedLabels, inertia, best_n_iter) = k_means(X, n_clusters, n_init=1, return_n_iter=True)
print('Number of iterations: %s' % (best_n_iter))
# Find the correct predictions, the permutations that maximizes the accuracy:
# predictedLabels = kmeans.labels_
trueLabels = y
adjustedRandScore = metrics.adjusted_rand_score(trueLabels, predictedLabels)
print('KMeans adjusted rand score: %s' % (adjustedRandScore))
tColour = tuple([0, 1, 0])
plt.scatter(X[y == predictedLabels, 0], X[y == predictedLabels, 1], c=tColour, alpha=0.5)
tColour = tuple([1, 0, 0])
plt.scatter(X[y != predictedLabels, 0], X[y != predictedLabels, 1], c=tColour, alpha=0.5)
plt.show()
def KMeansCluster(matrix):
"""
Performs the K-Means cluster given a matrix of data
@param[in]: matrix, List of List(s)
"""
# Possibly need to scale the data first
data = scale(matrix)
# Approximate the number of clusters using c = root(n/2)
# num_clusters = int(sqrt(len(matrix) / 2))
num_clusters = 5
number_init = 10 # Default
number_iter = 300
num_cpus = 2
print "==================="
print "Training KMeans with (num_clusters, num_init, num_iters, num_cpus)"
print num_clusters, number_init, number_iter, num_cpus
# estimator = KMeans(init='k-means++', n_clusters = num_clusters, n_init = number_init)
# estimator.fit(data)
# clusters = k_means(data, n_clusters = num_clusters, max_iter=number_iter, n_init = number_iter,
# init='k-means++', n_jobs = num_cpus)
clusters = k_means(data, n_clusters = num_clusters, max_iter=number_iter, n_init = number_iter, n_jobs = num_cpus)
return clusters
def kmeans_estimate(x):
labels = k_means(x, 2)[1]
# print labels
cluster = [[], []]
for i in xrange(len(labels)):
cluster[labels[i]].append(x[i])
cluster[0] = np.array(cluster[0])
cluster[1] = np.array(cluster[1])
pi = len(cluster[0]) * 1.0 / len(labels)
pis = [pi, 1 - pi]
means = []
sigmas = []
for i in xrange(2):
curr = cluster[i]
mean = np.average(curr, axis=0)
means.append(mean)
sigma = np.zeros((2, 2))
for i in xrange(len(curr)):
y = np.reshape(curr[i] - mean, (2, 1))
sigma += np.dot(y, y.T)
sigma /= len(curr)
sigmas.append(sigma)
pis = np.array(pis)
means = np.array(means)
sigmas = np.array(sigmas)
print means
return pis, means, sigmas
def clusterValidity(X, y):
# Maximum number of clusters:
K = 10
# Allocate variables:
Rand = np.zeros((K, ))
Jaccard = np.zeros((K, ))
NMI = np.zeros((K, ))
for k in range(K):
# run K-means clustering:
#cls = Pycluster.kcluster(X,k+1)[0]
centroids, cls, inertia = k_means(X, k + 1)
# compute cluster validities:
Rand[k], Jaccard[k], NMI[k] = clusterval(y, cls)
# Plot results:
figure(1)
title('Cluster validity')
plot(np.arange(K) + 1, Rand)
plot(np.arange(K) + 1, Jaccard)
plot(np.arange(K) + 1, NMI)
ylim(-2, 1.1)
legend(['Rand', 'Jaccard', 'NMI'], loc=4)
show()
def main():
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs
n_centers = 3
X, y = make_blobs(n_samples=1000, centers=n_centers, n_features=2,
cluster_std=0.7, random_state=0)
# Run this K-Means
import kmeans
t0 = time.time()
y_pred, centers, obj_val_seq = kmeans.kmeans(X, n_centers)
t1 = time.time()
print("Final obj val: {}".format(obj_val_seq[-1]))
print("Time taken (this implementation): {}".format(t1 - t0))
# Run scikit-learn's K-Means
from sklearn.cluster import k_means
t0 = time.time()
centers, y_pred, obj_val = k_means(X, n_centers, random_state=0)
t1 = time.time()
print("Final obj val: {}".format(obj_val))
print("Time taken (Scikit, 1 job): {}".format(t1 - t0))
# Plot change in objective value over iteration
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(obj_val_seq, 'b-', marker='*')
fig.suptitle("Change in K-means objective value across iterations")
ax.set_xlabel("Iteration")
ax.set_ylabel("Objective value")
fig.show()
# Plot data
from itertools import cycle
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
fig = plt.figure(figsize=plt.figaspect(0.5)) # Make twice as wide to accomodate both plots
ax = fig.add_subplot(121)
ax.set_title("Data with true labels and final centers")
for k, color in zip(range(n_centers), colors):
ax.plot(X[y==k, 0], X[y==k, 1], color + '.')
initial_centers = kmeans.init_centers(X, n_centers, 2) # This is valid because we always use the same random seed.
# Plot initial centers
for x in initial_centers:
ax.plot(x[0], x[1], "mo", markeredgecolor="k", markersize=8)
# Plot final centers
for x in centers:
ax.plot(x[0], x[1], "co", markeredgecolor="k", markersize=8)
# Plot assignments
colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
ax = fig.add_subplot(122)
ax.set_title("Data with final assignments")
for k, color in zip(range(n_centers), colors):
ax.plot(X[y_pred==k, 0], X[y_pred==k, 1], color + '.')
fig.tight_layout()
fig.gca()
fig.show()
def DrawSsePlot(self, title, data, random_state):
chart_folder = self.dir_folder + title + ".png"
chart_folder_re = self.upload_folder + title + ".png"
sse = [] # 手肘法则
lunkuo = [] # 轮廓系数存放距离
start, end = 3, 15
for i in range(start, end):
km = k_means(data, n_clusters=i, random_state=random_state)
sse.append(km[2])
lunkuo.append(silhouette_score(data, km[1], metric='euclidean'))
fig, ax1 = plt.subplots(figsize=(10, 7))
ax2 = ax1.twinx()
lns1 = ax1.plot(range(start, end), sse, 'o-', c='g', label='zhou-bu')
lns2 = ax2.plot(range(start, end),
lunkuo,
'o-',
c='r',
label='lun-kuo')
new_ticks = np.linspace(start, end, end - start + 1)
plt.xticks(new_ticks)
lns = lns1 + lns2
labs = [l.get_label() for l in lns]
ax1.legend(lns, labs, loc=0)
ax1.set_xlabel('K')
ax1.set_ylabel('SSE')
ax2.set_ylabel('LUN-KUO-INDEX')
plt.savefig(chart_folder)
plt.savefig(chart_folder_re)
plt.cla()
plt.clf()
return chart_folder
def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters,
sample_weight=None,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert_equal(centers.shape, (n_clusters, n_features))
labels = labels
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignment are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(inertia, 0.0)
# check warning when centers are passed
assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters,
sample_weight=None, init=centers)
# to many clusters desired
assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1,
sample_weight=None)
# kmeans for algorithm='elkan' raises TypeError on sparse matrix
assert_raise_message(TypeError, "algorithm='elkan' not supported for "
"sparse input X", k_means, X=X_csr, n_clusters=2,
sample_weight=None, algorithm="elkan")
def load_semantic_map():
target_mask_img = img_to_array(
load_img(target_mask_path, target_size=(img_nrows, img_ncols)))
style_mask_img = img_to_array(
load_img(style_mask_path, target_size=(img_nrows, img_ncols)))
if K.image_dim_ordering() == 'th':
mask_vecs = np.vstack([
style_mask_img.reshape((3, -1)).T,
target_mask_img.reshape((3, -1)).T
])
else:
mask_vecs = np.vstack([
style_mask_img.reshape((-1, 3)),
target_mask_img.reshape((-1, 3))
])
#_, labels = vq.kmeans2(mask_vecs, nb_labels, missing='raise')
_, labels, _ = k_means(mask_vecs.astype("float64"), nb_labels)
style_mask_label = labels[:img_nrows * img_ncols].reshape(
(img_nrows, img_ncols))
target_mask_label = labels[img_nrows * img_ncols:].reshape(
(img_nrows, img_ncols))
style_mask = np.stack([style_mask_label == r for r in range(nb_labels)],
axis=0)
target_mask = np.stack([target_mask_label == r for r in range(nb_labels)],
axis=0)
return np.expand_dims(style_mask, axis=0), np.expand_dims(target_mask,
axis=0)
def generate_cluster(self):
for i in range(self.size):
cod = self.get_coordinate()
self.G.add_node(i, pos=cod)
for i in range(len(self.ls)):
self.dic[i] = []
for j in range(len(self.ls)):
val = self.get_dist(self.ls[i], self.ls[j])
self.dic[i].append(round(val))
self.cluster = DBSCAN(eps=4, min_samples=1).fit_predict(np.array(self.ls))
n_clus = max(self.cluster) + 1
temp = k_means(np.array(self.ls), n_clusters=n_clus)
self.centroid = temp[0]
self.cluster = temp[1]
for i in range(len(self.centroid)):
for j in range(2):
self.centroid[i][j] = int(round(self.centroid[i][j]))
for i in range(max(self.cluster) + 1):
self.final[i] = []
for j in range(len(self.cluster)):
if (i == self.cluster[j]):
self.final[i].append(j)
for i in range(len(self.final)):
self.cluster_head.append(self.final[i][self.getClusterHead(self.final[i], self.centroid[i])])
return self.G
def getNPointsInRegion(self,
NPoints,
n=89,
s=-60,
e=180,
w=-180,
fidelity=1):
'''
:param NPoints: Number of coordinates you want to find
:param n: Northern Latitude
:param s: Southern Latitude
:param e: Eastern Longitude
:param w: Western Longitude
:param fidelity: granularity of the coordinate grid returned (smaller means more points)
:return: Pandas DataFrame of the points found to be land,
Pandas DataFrame the equidistant coordinates within this region
'''
globe_m = self.getPointsBetween(n, s, e, w, fidelity)
land = np.squeeze(np.asarray(globe_m[:, 2] == 1))
land_m = globe_m[land]
myCL = cluster.k_means(land_m, NPoints)
centroids = myCL[0]
return pd.DataFrame(land_m).iloc[:, 0:2], pd.DataFrame(
centroids).iloc[:, 0:2]
def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert_equal(centers.shape, (n_clusters, n_features))
labels = labels
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignment are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(inertia, 0.0)
# check warning when centers are passed
assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters,
init=centers)
# to many clusters desired
assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1)
def smythEmissionDistribution(pair): """ Given a pair (S: list of sequences, target_m: int), get the emission distribution for Smyth's "default" HMM. target_m is an upper bound on the number of states -- if we can only have m' distinct observation values, then the distribution for a m' state HMM is returned. @param pair: A tuple of the form (S: list of sequences, m: int) @return: The corresponding emission distribution encoded as a list of (mu, stddev) pairs """ S, target_m = pair merged, distinct = prepareSeqs(S) m_prime = min(target_m, len(distinct)) centroids, labels, inertia = k_means(merged, m_prime, init='k-means++') clusters = partition(merged, labels) B = [] has_zero = False for cluster in clusters: assert len(cluster) > 0 mu = mean(cluster) stddev = std(cluster) B.append((mu, stddev)) if stddev < 0.001: has_zero = True return (B, labels, has_zero)
def get_jackknife_randoms(N_jack,
catalog_data,
generate_randoms,
ra='ra',
dec='dec'):
"""
Computes the jackknife regions and random catalogs for each region
Parameters
----------
N_jack : number of regions
catalog_data : input catalog
generate_randoms: function to generate randoms (eg self.generate_processed_randoms)
Returns
-------
jack_labels: array of regions in catalog data
randoms: dict of randoms labeled by region
"""
#cluster
nn = np.stack((catalog_data[ra], catalog_data[dec]), axis=1)
_, jack_labels, _ = k_means(n_clusters=N_jack, random_state=0, X=nn)
randoms = {}
for nj in range(N_jack):
catalog_data_jk = dict(
zip(catalog_data.keys(),
[v[(jack_labels != nj)] for v in catalog_data.values()]))
rand_cat, rr = generate_randoms(
catalog_data_jk) #get randoms for this footprint
randoms[str(nj)] = {'ran': rand_cat, 'rr': rr}
return jack_labels, randoms
def mosquito_init(sample_space, dim_count, num_particles, num_swarms=5):
if num_swarms > num_particles:
num_swarms = num_particles
low_bound, high_bound = sample_space
mosquitos = np.random.uniform(low_bound,
high_bound,
size=(num_particles, dim_count))
#leaders, swarms = kmeans(mosquitos, num_centroids=num_swarms)
#print swarms
#print
res = k_means(mosquitos, num_swarms)
swarm_idx = res[1]
swarms = [[] for _ in xrange(num_swarms)]
for idx in xrange(len(swarm_idx)):
swarms[swarm_idx[idx]].append(mosquitos[idx])
# print swarms
# exit(0)
starvation = [0.0 for _ in xrange(num_swarms)]
return [(np.inf, None)
for _ in xrange(num_swarms)], swarms, starvation, num_particles
def determine_k(item):
print ''
nums = 10 #平均轮廓系数计算次数
max_k = 10
min_k = 2
result = [0] * (max_k - min_k)
data = data_pre(item)
for j in range(nums):
scs = []
for i in range(min_k, max_k):
[centroid, label, inertia] = cluster.k_means(data, i)
sc = silhouette_score(data, label, metric='euclidean') # 平均轮廓系数
scs.append(sc)
result[i - min_k] += sc
for i in range(len(scs)):
result[i] = result[i] / nums
temp = pd.DataFrame(result)
temp.to_csv('data/Silhouette' + str(item) + '.csv',
index=False) #index设置是否显示行号
plt.plot(result, 'rx-')
plt.title('scs' + str(nums))
plt.xlabel('the numbers of clusters')
plt.ylabel('Silhouette Coefficient')
plt.savefig('img/Silhouette Coefficient.png')
#plt.show()
plt.close()
def example_4(): """ compare to scikitlearn implementation of kmeans """ import sklearn.cluster as skc import time ndata = 50000 dimension = 10 ncentroids = 1000 data = npr.randn(ndata, dimension).astype(np.float64) centroids0 = data[0:ncentroids, :] t0 = time.time() kmeans.get_clustering(X = data, init = centroids0, n_clusters = ncentroids, algorithm = 'auto', verbose = 1, n_threads = 1) t1 = time.time() sklearner = skc.k_means(X = data, n_clusters = ncentroids, max_iter = 1000, n_init = 1, init = centroids0, precompute_distances = False, verbose = True, n_jobs = 1, return_n_iter = True, tol = 0.0) t2 = time.time() kmeans_time = t1 - t0 sklearner_time = t2 - t1 print "sklearn : ", sklearner_time, " s" print "this kmeans: ", kmeans_time, " s"
def cluster_samples(args):
workdir = args.workdir
analysis_dir = os.path.join(workdir, "analysis")
if not os.path.exists(analysis_dir):
os.makedirs(analysis_dir)
project_summary = deserialize(os.path.join(workdir,
PROJECT_SUMMARY_FLNAME))
model_fl = os.path.join(workdir, "models", "pca.pkl")
model = joblib.load(model_fl)
projected = model[PROJECTION_KEY]
components = map(lambda idx: idx - 1, args.components)
selected = projected[:, components]
features = read_features(workdir)
_, labels, inertia = k_means(selected, args.n_clusters, n_jobs=-2)
fig_flname = os.path.join(analysis_dir,
"clusters_%s.tsv" % args.n_clusters)
clusters = defaultdict(list)
for name, cluster in zip(features.sample_labels, labels):
clusters[cluster].append(name)
with open(fig_flname, "w") as fl:
for cluster, samples in clusters.iteritems():
fl.write(str(cluster))
fl.write(",")
fl.write(",".join(samples))
fl.write("\n")
def sweep_clusters(args):
workdir = args.workdir
figures_dir = os.path.join(workdir, "figures")
if not os.path.exists(figures_dir):
os.makedirs(figures_dir)
project_summary = deserialize(os.path.join(workdir,
PROJECT_SUMMARY_FLNAME))
model_fl = os.path.join(workdir, "models", "pca.pkl")
model = joblib.load(model_fl)
projected = model[PROJECTION_KEY]
components = map(lambda idx: idx - 1, args.components)
selected = projected[:, components]
features = read_features(workdir)
inertia_values = []
for k in args.n_clusters:
print "Clustering with %s clusters" % k
_, _, inertia = k_means(selected, k, n_jobs=-2)
inertia_values.append(inertia)
plt.plot(args.n_clusters, inertia_values, "k.-")
plt.xlabel("Number of Clusters", fontsize=16)
plt.ylabel("Inertia", fontsize=16)
fig_flname = os.path.join(figures_dir, "cluster_inertia")
for dim in args.components:
fig_flname += "_%s" % dim
fig_flname += ".png"
plt.savefig(fig_flname, DPI=300)
def cluster_GSOM(self, gsom_map, n_clusters=2):
"""
Parameters
----------
gsom_map : growing self organizing map
2D array of weight vectors in SOM.
n_clusters : number of clusters.
Returns
-------
gsom_list : list
list of the gsom nodes
centroid : list
cluster centroids.
labels : list
cluster label w.r.t. gsom node data-point as in gsom_list
"""
gsom_list = self._gsom_to_array(gsom_map)
clf = k_means(gsom_list, n_clusters=n_clusters)
centroids = clf[0]
labels = clf[1]
return gsom_list, centroids, labels
def test_k_means_function():
# test calling the k_means function directly
# catch output
old_stdout = sys.stdout
sys.stdout = StringIO()
try:
cluster_centers, labels, inertia = k_means(X, n_clusters=n_clusters,
sample_weight=None,
verbose=True)
finally:
sys.stdout = old_stdout
centers = cluster_centers
assert_equal(centers.shape, (n_clusters, n_features))
labels = labels
assert_equal(np.unique(labels).shape[0], n_clusters)
# check that the labels assignment are perfect (up to a permutation)
assert_equal(v_measure_score(true_labels, labels), 1.0)
assert_greater(inertia, 0.0)
# check warning when centers are passed
assert_warns(RuntimeWarning, k_means, X, n_clusters=n_clusters,
sample_weight=None, init=centers)
# to many clusters desired
assert_raises(ValueError, k_means, X, n_clusters=X.shape[0] + 1,
sample_weight=None)
# kmeans for algorithm='elkan' raises TypeError on sparse matrix
assert_raise_message(TypeError, "algorithm='elkan' not supported for "
"sparse input X", k_means, X=X_csr, n_clusters=2,
sample_weight=None, algorithm="elkan")
def action_execute_button_clicked(self):
#打开影像
input_img = gdal.Open(self.input_file_path.text())
img_rows = input_img.RasterYSize
img_cols = input_img.RasterXSize
img_bands = input_img.RasterCount
img_geotrans = input_img.GetGeoTransform()
img_proj = input_img.GetProjection()
# 将影像转为k_means函数接受的数据格式
input_features = []
for i in range(1, img_bands + 1):
band_img = input_img.GetRasterBand(i).ReadAsArray(
0, 0, img_cols, img_rows)
input_features.append(band_img.reshape(-1))
input_features = np.array(input_features).T
#执行k_means算法
kmeans_result = k_means(input_features,
int(self.cluster_num.currentText()),
max_iter=int(self.iter_num.currentText()))
#将各样本点灰度值转为对应聚类中心灰度值
cluster_centers, clustered_points, _ = kmeans_result
output_feature = []
for index, item in enumerate(clustered_points):
while item > len(self.color_list) - 1:
self.color_list.append(list(np.random.randint(256, size=3)))
output_feature.append(self.color_list[item])
output_feature = np.array(output_feature).T
output_feature = np.array(
list(map(lambda x: x.reshape((img_rows, img_cols)),
output_feature)))
#输出聚类影像
driver = gdal.GetDriverByName("GTiff")
output_img = driver.Create(self.output_file_path.text(), img_cols,
img_rows, 3, gdal.GDT_Byte)
output_img.SetGeoTransform(img_geotrans)
output_img.SetProjection(img_proj)
for i in range(1, 4):
output_img.GetRasterBand(i).WriteArray(output_feature[i - 1])
del output_img
layer_legends = [] # 图例数组
for i in range(len(cluster_centers)):
layer_legends.append({
'name':
'Cluster' + str(i + 1),
'color':
QColor(self.color_list[i][0], self.color_list[i][1],
self.color_list[i][2])
})
if (QMessageBox.question(self, "消息框", "聚类完成，是否将结果添加到图层？",
QMessageBox.Yes | QMessageBox.No,
QMessageBox.Yes) == QMessageBox.Yes):
self.add_layer_signal.emit(self.output_file_path.text(),
layer_legends)
def testRealData():
sDirname = os.path.dirname(os.path.abspath(__file__))
dfAspen = pd.read_csv(
os.path.join(sDirname, '..', 'datasets', 'aspen.csv'), ';')
dfAspen = dfAspen.dropna()
dfAspen = dfAspen.reindex(np.random.permutation(dfAspen.index))
asCities = dfAspen['address_city'].unique()
X = dfAspen[['location_coordinates_0',
'location_coordinates_1']].as_matrix()
Y = dfAspen['address_city'].as_matrix()
Y = np.array([asCities.tolist().index(sCity) for sCity in Y
]) # Convert array of strings to sequential integers
# Convert coordinates to x,y grid using the Equirectangular projection
X = X * np.pi / 180
fMeanLatitude = X.mean(0)[0]
X[:, 0] = X[:, 0] * np.cos(fMeanLatitude)
# First x, then y:
Xaux = np.copy(X)
X[:, 1] = Xaux[:, 0]
X[:, 0] = Xaux[:, 1]
n_clusters = len(asCities)
# K-means:
(centers, PredictedLabels, inertia,
best_n_iter) = k_means(X, n_clusters, n_init=1, return_n_iter=True)
adjustedRandScore = metrics.adjusted_rand_score(Y, PredictedLabels)
print('KMeans adjusted rand score: %s' % (adjustedRandScore))
print('Number of iterations: %s' % (best_n_iter))
# Seeded k-means with all seeds:
# Drop some seeds:
fRatio = 0
dMapping = {}
maxSeed = -1
SomeSeeds = np.repeat(-1, len(Y))
for i in range(len(Y)):
if np.random.rand() > fRatio:
continue
iCity = Y[i]
if iCity not in dMapping:
maxSeed += 1
dMapping[iCity] = maxSeed
SomeSeeds[i] = dMapping[iCity]
sm = SeededKMeans(n_clusters=n_clusters, max_iter=2000, verbose=1)
sm.fit(X, SomeSeeds)
PredictedLabels = sm.predict(X)
adjustedRandScore = metrics.adjusted_rand_score(Y, PredictedLabels)
print('Seeded KMeans adjusted rand score: %s' % (adjustedRandScore))
for predictedLabel in np.unique(PredictedLabels):
plt.scatter(X[PredictedLabels == predictedLabel, 0],
X[PredictedLabels == predictedLabel, 1],
color=(np.random.rand(), np.random.rand(),
np.random.rand()),
alpha=1)
plt.show()
def get_point_centroids(indata,K,D):
mean = numpy.zeros((indata.shape[1],D))
for n in xrange(0,(indata.shape[1])):
for i in xrange(0,(indata.shape[2])):
for j in xrange(0,D):
mean[n][j] = mean[n][j] + indata[j][n][i]
mean[n] = mean[n]/(indata.shape[2])
(centroids,x,y)=k_means(mean,K) #random order. change n_jobs to speed up
return centroids
def twoClassTrain(self,data1,data2): #y is a N * 1 matrix data = data1 + data2 y = [[1 if d[0] == data1[0][0] else -1] for d in data] X = [d[1:] for d in data] centroid, label, inertia = cluster.k_means(X,self.k) phi = self.genTransferMatrix(X,centroid) w = numpy.dot(numpy.linalg.pinv(phi),y) return w, centroid
def learn(self, Xtrain, ytrain):
""" Learns using the traindata """
Xless = Xtrain[:,self.features]
lam = 100
self.centers = cluster.k_means(Xless, 10)[0]
Xless = np.dot(Xless, self.centers.T)
self.weights = np.dot(np.dot(np.linalg.inv(np.dot(Xless.T,Xless) + lam*np.identity(Xless.shape[1])), Xless.T),ytrain)
def kmeans(xs, k):
assert xs.ndim == 2
try:
from sklearn.cluster import k_means
_, labels, _ = k_means(xs.astype("float64"), k)
except ImportError:
from scipy.cluster.vq import kmeans2
_, labels = kmeans2(xs, k, missing='raise')
return labels
def initial_means(self,X,n_clusters):
mean_KMeans_initial,label, intertia = k_means(X, n_clusters, random_state = 1)
KernelKMeans_model = KernelKMeans(n_clusters=n_clusters, random_state=1,
kernel="rbf", gamma=None, coef0=1,
verbose=0)
KernelKMeans_model.fit(X)
mean_KernelKMeans_initial = self.pre_image(X, KernelKMeans_model.labels_, KernelKMeans_model.gamma, n_clusters, 100)
return mean_KMeans_initial , mean_KernelKMeans_initial
def learn(self, Xtrain, ytrain):
""" Learns using the traindata """
Xless = Xtrain[:,self.features]
lam = 100 # set lambda for regularizer coefficient
self.centers = cluster.k_means(Xless, 10)[0]
Xless = (1 + np.dot(Xless, self.centers.T))**self.d
self.weights = np.dot(np.dot(np.linalg.inv(np.dot(Xless.T,Xless) + lam*np.identity(Xless.shape[1])), Xless.T),ytrain)
def gap(data, refs=None, nrefs=20, ks=range(1,11)):
"""
I: NumPy array, reference matrix, number of reference boxes, number of clusters to test
O: Gaps NumPy array, Ks input list
Give the list of k-values for which you want to compute the statistic in ks. By Gap Statistic
from Tibshirani, Walther.
"""
shape = data.shape
if not refs:
tops = data.max(axis=0)
bottoms = data.min(axis=0)
dists = scipy.matrix(scipy.diag(tops - bottoms))
rands = scipy.random.random_sample(size=(shape[0], shape[1], nrefs))
for i in range(nrefs):
rands[:, :, i] = rands[:, :, i] * dists + bottoms
else:
rands = refs
gaps = scipy.zeros((len(ks),))
for (i,k) in enumerate(ks):
k_means_args_dict['n_clusters'] = k
kmeans = k_means(**k_means_args_dict)
kmeans.fit(data)
(cluster_centers, point_labels) = kmeans.cluster_centers_, kmeans.labels_
disp = sum([dst(data[current_row_index, :], cluster_centers[point_labels[current_row_index],:]) for current_row_index in range(shape[0])])
refdisps = scipy.zeros((rands.shape[2],))
for j in range(rands.shape[2]):
kmeans = k_means(**k_means_args_dict)
kmeans.fit(rands[:, : ,j])
(cluster_centers, point_labels) = kmeans.cluster_centers_, kmeans.labels_
refdisps[j] = sum([dst(rands[current_row_index,:,j], cluster_centers[point_labels[current_row_index],:]) for current_row_index in range(shape[0])])
#let k be the index of the array 'gaps'
gaps[i] = scipy.mean(scipy.log(refdisps)) - scipy.log(disp)
return ks, gaps
def cluster_spatial_data(X, n_parcels, xyz=None, shape=None, mask=None,
method='ward', verbose=False):
"""Cluster the data using Ward's algorithm
Parameters
==========
X: array of shape(n_voxels, n_subjects)
the functional data, across subjects
n_parcels: int, the desired number of parcels
xyz: array of shape (n_voxels, 3), optional
positions of the voxels in grid coordinates
shape: tuple: the domain shape (assuming a grid structure), optional
alternative specification of positions
mask: arbitrary array of arbitrary dimension,optional
alternative specification of positions
method: string, one of ['ward', 'spectral', 'kmeans'], optional
clustering method
Returns
=======
label: array of shape(n_voxels): the resulting cluster assignment
Note
====
One of xyz, shape or mask needs to be provided
"""
from sklearn.cluster import spectral_clustering, k_means
if mask is not None:
connectivity = grid_to_graph(*shape, mask=mask)
elif shape is not None:
connectivity = grid_to_graph(*shape)
elif xyz is not None:
from sklearn.neighbors import kneighbors_graph
n_neighbors = 2 * xyz.shape[1]
connectivity = kneighbors_graph(xyz, n_neighbors=n_neighbors)
else:
raise ValueError('One of mask, shape or xyz has to be provided')
if n_parcels == 1:
return np.zeros(X.shape[0])
if method == 'ward':
connectivity = connectivity.tocsr()
ward = Ward(n_clusters=n_parcels, connectivity=connectivity).fit(X)
label = ward.labels_
elif method == 'spectral':
i, j = connectivity.nonzero()
sigma = np.sum((X[i] - X[j]) ** 2, 1).mean()
connectivity.data = np.exp(- np.sum((X[i] - X[j]) ** 2, 1) /
(2 * sigma))
label = spectral_clustering(connectivity, n_clusters=n_parcels)
elif method == 'kmeans':
_, label, _ = k_means(X, n_parcels)
else:
raise ValueError('Unknown method for parcellation')
return label
def cluster_index_2(X):
global_mean = np.mean(X, axis=0)
sum_squared_distances = (((X - global_mean) ** 2).sum(axis=1)).sum()
# Sum of squared distances of each sample from the global mean
centroids, labels, inertia = k_means(X, 2)
ci = inertia / sum_squared_distances
return ci, labels
def KMEANS(data, k):
if data.shape[0] < 20000:
centroids, cluster_IDs, _ = k_means(data, k, init = 'k-means++', precompute_distances = 'auto', n_init = 20, max_iter = 200)
else:
mbkm = MiniBatchKMeans(k, 'k-means++', max_iter = 100, batch_size = data.shape[0] / k, n_init = 20)
mbkm.fit(data)
centroids = mbkm.cluster_centers_
cluster_IDs = mbkm.labels_
return centroids, cluster_IDs
def smythEmissionDistribution(pair):
"""
Given a pair (S: list of sequences, target_m: int), get the emission
distribution for Smyth's "default" HMM. target_m is an upper bound on the
number of states -- if we can only have m' distinct observation values, then
the distribution for a m' state HMM is returned.
@param pair: A tuple of the form (S: list of sequences, target_m: int)
@return: (B, labels, has_zero), where:
* S', obs = concat(S), set(S)
* m' = min(target_m, len(obs))
* [C_0,...,C_{m'-1}] = result of clustering S' with k-means.
* labels: tells which cluster each item in merged goes into; i.e.,
labels[i] = j, where S'[i] belongs to cluster C_j.
* B[i] = (mean(C_i), stddev(C_i)).
* has_zero = True if there is i such that B[i][1] ~= 0.0.
"""
S, target_m = pair
# merged list of 1d vectors, set of distinct observation values
merged, distinct = prepareSeqs(S)
# m_prime is min of either target_m or the number of distinct obs values
m_prime = min(target_m, len(distinct))
# k-means partitions merged into m_prime clusters [C_0,...,C_{m'-1}].
# centroids = [c_0,...,c_{m'-1}]: cluster centers; i.e., c_i is the center
# of C_j.
# labels: tells which cluster each item in merged goes into; i.e.,
# labels[i] = j, where merged[i] belongs to cluster C_j.
# inertia: sum of distances of samples to closest cluster center
# inertia = sum_{i=0}^{m'-1}(sum_{x in C_i} dist(x, c_i)).
centroids, labels, inertia = k_means(merged, m_prime, init='k-means++')
# takes labels and arranges merged into
# a list of lists, each of which contains the series from one cluster
# clusters = [C_0,..,C_{m'-1}]
clusters = partition(merged, labels)
# Compute (B, labels, has_zero), where
# B[i] = (mean(C_i), stddev(C_i)).
# has_zero = True if there is i such that B[i][1] ~= 0.0.
B = []
has_zero = False
for cluster in clusters:
assert len(cluster) > 0
mu = mean(cluster)
stddev = std(cluster)
B.append((mu, stddev))
if stddev < 0.001:
has_zero = True
return (B, labels, has_zero)
def test_k_means(self):
iris = datasets.load_iris()
df = pdml.ModelFrame(iris)
result = df.cluster.k_means(3, random_state=self.random_state)
expected = cluster.k_means(iris.data, 3, random_state=self.random_state)
self.assertEqual(len(result), 3)
self.assert_numpy_array_almost_equal(result[0], expected[0])
self.assertTrue(isinstance(result[1], pdml.ModelSeries))
self.assert_index_equal(result[1].index, df.index)
self.assert_numpy_array_equal(result[1].values, expected[1])
self.assertAlmostEqual(result[2], expected[2])
def kcluster(content, num_cluster = 6, num_key = 15, single = False, num_top = -1):
# num_key is the number of keyword that we extract from a cluster
# we can find the union of the extracted keys from each cluster
one_hot_tokens, weight_array, mapping_back, postprocess_sentences, C = get_rakeweight_data(content)
# in case we have less vector than cluste number
num_cluster = min(num_cluster, len(weight_array))
token_weights = defaultdict(float)
keyword_weights = defaultdict(float)
k_clusters = cluster.k_means(weight_array, num_cluster)[0]
union_array = []
keywords = []
num_key = min(num_key, len(one_hot_tokens))
for i,vec in enumerate(k_clusters):
tmp = sorted(range(len(vec)), key=lambda i: vec[i])[-num_key:]
union_array = list(set(tmp) | set(union_array))
for ind in tmp:
token = one_hot_tokens[ind]
degree = sum(C[token].values())
freq = C[token][token]
# currently degree. update to different weight scheme if needed
token_weights[token] += float(degree)
for ind in union_array:
token = one_hot_tokens[ind]
keyword = mapping_back[token]
keywords.append(keyword.encode('ascii'))
# for all tokens that map to keyword
keyword_weights[keyword] += token_weights[token]
keywords = list(set(keywords))
if single:
keywords = set(keywords)
if (num_top < 0):
num_top = len(keywords)
return random.sample(keywords, min(len(keywords), num_top))
# get keyphrases
keyphrases,keyphrase_freq = get_keyphrases(keywords, postprocess_sentences)
# keyphrases_weights = sum keyword_weights[word] / total_words
# for all words in keywords
keyphrases_weights = get_keyphrase_weights(keyphrases, keyword_weights, keyphrase_freq)
keyword_weights.update(keyphrases_weights)
if num_top < 0:
num_top = len(keyword_weights)/3
top_keywords = sorted(keyword_weights, key=keyword_weights.get, reverse=True)[:min(num_top, len(keyword_weights))]
# for keyword in top_keywords:
# print(keyword + ' '*(40-len(keyword)) + str(keyword_weights[keyword]))
return top_keywords
def call_k_means(data, n_clusters):
"""
k_means(X, n_clusters, init='k-means++', precompute_distances=True, n_init=10,
max_iter=300, verbose=False, tol=0.0001, random_state=None, copy_x=True, n_jobs=1)
返回：
centroid
label
inertia
"""
k_clusters, k_labels, k_dis = cluster.k_means(data, n_clusters)
print "中心点坐标：\n", k_clusters
print "类标签：\n", k_labels
print "距离：\n", k_dis
return k_labels
def kcluster(mapping_back, num_cluster, weight_array, one_hot_tokens, num_key = 3):
# num_key is the number of keyword that we extract from a cluster
# we can find the union of the extracted keys from each cluster
# in case we have less vector than cluste number
num_cluster = min(num_cluster, len(weight_array))
k_clusters = cluster.k_means(weight_array, num_cluster)[0]
union_array = []
num_key = min(num_key, len(one_hot_tokens))
for i,vec in enumerate(k_clusters):
tmp = sorted(range(len(vec)), key=lambda i: vec[i])[-num_key:]
union_array = list(set(tmp) | set(union_array))
res = []
for ind in union_array:
res.append(mapping_back[one_hot_tokens[ind]])
return res
def learn(self, Xtrain, ytrain):
""" Learns using the traindata """
Xless = Xtrain[:,self.features]
lam = 1000 # set regularizer coeff
num = Xless.shape[0]
XKer = np.zeros((num, 10))
self.centers = cluster.k_means(Xless, 10)[0]
#transform Data
for i in range(num):
for j in range(10):
XKer[i, j] = np.exp(-(np.linalg.norm(Xless[i, ] - self.centers[j]))/2*self.s**2)
self.weights = np.dot(np.dot(np.linalg.inv(np.dot(XKer.T,XKer) + lam*np.identity(XKer.shape[1])), XKer.T),ytrain)
