def km(tx, ty, rx, ry, add="", times=5):
#this does the exact same thing as the above
errs = []
checker = KM(n_clusters=2)
checker.fit(ry)
truth = checker.predict(ry)
# so we do this a bunch of times
for i in range(2,times):
clusters = {x:[] for x in range(i)}
clf = KM(n_clusters=i)
clf.fit(tx) #fit it to our data
test = clf.predict(tx)
result = clf.predict(rx) # and test it on the testing set
for index, val in enumerate(result):
clusters[val].append(index)
mapper = {x: round(sum(truth[v] for v in clusters[x])/float(len(clusters[x]))) if clusters[x] else 0 for x in range(i)}
processed = [mapper[val] for val in result]
errs.append(sum((processed-truth)**2) / float(len(ry)))
plot([0, times, min(errs)-.1, max(errs)+.1],[range(2, times), errs, "ro"], "Number of Clusters", "Error Rate", "KMeans clustering error", "KM"+add)
td = np.reshape(test, (test.size, 1))
rd = np.reshape(result, (result.size, 1))
newtx = np.append(tx, td, 1)
newrx = np.append(rx, rd, 1)
nn(newtx, ty, newrx, ry, add="onKM"+add)
def KMeansRatio(self):
'''
Type: K-Means
Y-axis: No Reaction
X-axis: Reaction
'''
if self.authenticated:
from sklearn.cluster import KMeans as KM
algorithm = KM(n_clusters=2)
categories = algorithm.fit_predict(self.allCoord)
plt.scatter(self.allCoord[categories == 0, 0],
self.allCoord[categories == 0, 1],
c="green")
plt.scatter(self.allCoord[categories == 1, 0],
self.allCoord[categories == 1, 1],
c="red")
plt.scatter(algorithm.cluster_centers_[:, 0],
algorithm.cluster_centers_[:, 1],
c="black",
marker="*")
for i, txt in enumerate(self.labels):
plt.annotate(txt, (self.allCoord[i][0], self.allCoord[i][1]))
plt.ylabel("NO REACTION")
plt.xlabel("REACTION")
plt.annotate("NO INFLAMMATION", algorithm.cluster_centers_[0])
plt.annotate("CAUSES INFLAMMATION", algorithm.cluster_centers_[1])
plt.title("K-Means: Reaction, No Reaction")
plt.show()
def KMeansPartPercent(self):
'''
Type: K-Means
Y-axis: % Reactions
X-axis: # Reactions
'''
if self.authenticated:
from sklearn.cluster import KMeans as KM
algorithm = KM(n_clusters=2)
# partPercent = np.array([np.array([x, percent]) for j in self.stuff for _, x, _, percent in j])
categories = algorithm.fit_predict(self.partPercent)
plt.scatter(self.partPercent[categories == 0, 0],
self.partPercent[categories == 0, 1],
c="green")
plt.scatter(self.partPercent[categories == 1, 0],
self.partPercent[categories == 1, 1],
c="red")
plt.scatter(algorithm.cluster_centers_[:, 0],
algorithm.cluster_centers_[:, 1],
c="black",
marker="*")
for i, txt in enumerate(self.labels):
plt.annotate(txt,
(self.partPercent[i][0], self.partPercent[i][1]))
plt.ylabel("PERCENT")
plt.xlabel("NUM OF INFLAMS")
plt.annotate("NO INFLAMMATION", algorithm.cluster_centers_[0])
plt.annotate("CAUSES INFLAMMATION", algorithm.cluster_centers_[1])
plt.title("K-Means: # Reactions, % Reactions")
plt.show()
def extractColors(stream, maxColor):
pixData = np.array(stream)
h, w, d = pixData.shape
data = np.reshape(pixData, (h * w, d))
km = KM(n_clusters=maxColor)
km.fit(data)
theme = np.array(km.cluster_centers_, dtype=np.uint8).tolist()
return getrgbAndHex(theme)
def cluster(self, inputs):
t = time()
helper._print('Training clusters (KMeans)...')
kmeans = KM(n_clusters=self.num_clusters, init=self.cluster_init, max_iter=1000, tol=0.000001)
cluster_pred = kmeans.fit_predict(inputs)
helper._print(f'Done training clusters. Finished in {int((time() - t)/60)} minutes and {int((time() - t) % 60)} seconds!')
return cluster_pred
def __init__(self, data, k, t, iter, maxE):
#if t == 0:
# t = 'k-means++'
#else:
# t = 'random'
self.kmean = KM(k, t, iter, maxE).fit(np.array(data))
self.labels = self.kmean.labels_
self.clusters = self.kmean.cluster_centers_
def __init__(self, pixData, maxColor, useSklearn=True):
super(KMeans, self).__init__()
h, w, d = pixData.shape
self.pixData = np.reshape(pixData, (h * w, d))
self.maxColor = maxColor
if useSklearn:
self._KMeans = KM(n_clusters=maxColor)
else:
self._KMeans = KMDiy(n_clusters=maxColor)
def do_KM(self, letter, k, iter=200, dump=True, parr=-2):
if type(letter) == str:
l, v = self.find_by_start(letter)
km_obj = KM(n_clusters=k, max_iter=iter, n_jobs=parr)
results = km_obj.fit(v)
if dump == True:
filename = DT.now().strftime(
'%d%m%y-%H%M%S') + self.generate_filename() + '-k' + str(k)
self.dump(l, results.labels_, filename)
self.km_labels = l
self.km_clusters = results.labels_
print('Stored KM output in self.km_labels, self.km_clusters')
else:
self.km_labels = l
self.km_clusters = results.labels_
print('Stored KM output in self.km_labels, self.km_clusters')
if type(letter) == list:
l, v = self.find_by_list(letter)
km_obj = KM(n_clusters=k, max_iter=iter, n_jobs=parr)
results = km_obj.fit(v)
self.predicted_clusters = {}
for v, k in zip(l, results.labels_):
self.predicted_clusters[v] = k
self.pred_prep = sorted(self.predicted_clusters.items(),
key=itemgetter(0))
self.predicted = [i[1] for i in self.pred_prep]
if dump == True:
filename = DT.now().strftime(
'%d%m%y-%H%M%S') + self.generate_filename() + '-k' + str(k)
self.dump(l, results.labels_, filename)
self.km_labels = l
self.km_clusters = results.labels_
print('Stored KM output in {self}.km_labels, self.km_clusters')
else:
self.km_labels = l
self.km_clusters = results.labels_
print('Stored KM output in self.km_labels, self.km_clusters')
def set_bin_ranges_for_property(
self, property_to_entities: Dict[int, Set[Entity]],
class_to_entities: Dict[int, Set[Entity]],
property_to_timestamps: Dict[int,
List[TimeStamp]], property_id: int):
if not property_to_timestamps:
self.load_property_to_timestamps(property_to_timestamps,
property_id)
property_values = np.array([
ts.value for ts in property_to_timestamps[property_id]
]).reshape(-1, 1)
kmeans = KM(n_clusters=self.bin_count - 1).fit(property_values)
return list(
sorted([centroid[0] for centroid in kmeans.cluster_centers_]))
def findClusterCenters(data):
# prepare data
nonzero_xy = np.nonzero(data)
if len(nonzero_xy) != 2:
return
if nonzero_xy[0].size < 8:
return
processed_data = np.vstack((nonzero_xy[0], nonzero_xy[1])).transpose()
# K-Means algorithm
kmeans = KM(n_clusters = 8, max_iter=20, n_init = 5)
kmeans.fit(processed_data)
center_points = np.fliplr(kmeans.cluster_centers_)
return center_points
def KMeans(self):
return1 = self.printPoints()
if not return1:
return
algorithm = KM(n_clusters=2)
categories = algorithm.fit_predict(self.allCoord)
print(self.allCoord)
print(categories)
plt.scatter(self.allCoord[categories == 0, 0], self.allCoord[categories == 0, 1], c= "green")
plt.scatter(self.allCoord[categories == 1, 0],self.allCoord[categories == 1, 1], c="red")
plt.scatter(algorithm.cluster_centers_[:, 0], algorithm.cluster_centers_[:, 1], c= "black", marker="*")
print(len(self.labels), len(self.allCoord))
for i, txt in enumerate(self.labels):
plt.annotate(txt, (self.allCoord[i][0], self.allCoord[i][1]))
plt.annotate("NO INFLAMMATION", algorithm.cluster_centers_[0])
plt.annotate("CAUSES INFLAMMATION", algorithm.cluster_centers_[1])
plt.savefig("static/" + NAME)
self.src = NAME
def KMeansPercentTotal(self):
'''
Type: K-Means
Y-axis: % Reactions
X-axis: # Observations
'''
if self.authenticated:
from sklearn.cluster import KMeans as KM
algorithm = KM(n_clusters=2)
fig = plt.figure()
# partPercent = np.array([np.array([x, percent]) for j in self.stuff for _, x, _, percent in j])
categories = algorithm.fit_predict(self.percentTotal)
plt.scatter(self.percentTotal[categories == 0, 0],
self.percentTotal[categories == 0, 1],
c="green")
plt.scatter(self.percentTotal[categories == 1, 0],
self.percentTotal[categories == 1, 1],
c="red")
plt.scatter(algorithm.cluster_centers_[:, 0],
algorithm.cluster_centers_[:, 1],
c="black",
marker="*")
for i, txt in enumerate(self.labels):
plt.annotate(
txt, (self.percentTotal[i][0], self.percentTotal[i][1]))
plt.ylabel("PERCENT")
plt.xlabel("TOTAL")
plt.annotate("NO INFLAMMATION", algorithm.cluster_centers_[0])
plt.annotate("CAUSES INFLAMMATION", algorithm.cluster_centers_[1])
plt.title("K-Means: # Observations, % Reactions")
# plt.show()
# mpld3.show()
# plt.savefig()
tmpfile = BytesIO()
# plt.savefig('test.png')
fig.savefig(tmpfile, format='png')
encoded = base64.b64encode(tmpfile.getvalue())
html = '<img src=\'data:image/png;base64,{}\'>'.format(
encoded.decode("utf-8"))
with open('KMeansPercentTotal.html', 'w') as f:
f.write(html)
def ttsf(ID, CONTENTS):
length = len(ID)
wei = []
for content in CONTENTS:
cut = jieba.cut(content, cut_all=False)
words = (' '.join(cut))
wei.append(words)
vector = TfidfVectorizer()
tfidf = vector.fit_transform(wei)
d = tfidf.toarray()
k = 3
clf = KM(k)
hehe = clf.fit_predict(d)
results = []
for i in range(length):
if hehe[i] == hehe[0] and i != 0:
results.append(ID[i])
else:
None
print(results)
return results
def passl_local_graph_partial(site, loc_param_indices, params):
X = site.buff[loc_param_indices[0]]
K, rbf_sigma, local_graph_index, n_cluster, centers_index, point_cluster_index, inter_graph_index, member_id_index = params
nins = NN(K + 1, None, metric='euclidean').fit(X)
W = nins.kneighbors_graph(nins._fit_X, K + 1, mode='distance')
#W.data=W.data**2
W.data = np.exp(-W.data**2 / rbf_sigma)
W[np.diag_indices(W.shape[0])] = 0
#W[np.diag_indices(W.shape[0])]=0
site.buff[local_graph_index] = W
kins = KM(n_cluster)
point_cluster = kins.fit_predict(X)
site.buff[point_cluster_index] = point_cluster
site.buff[centers_index] = kins.cluster_centers_
#print(kins.cluster_centers_)
site.buff[inter_graph_index] = {}
member_id = []
for i in range(n_cluster):
member_id.append(np.where(point_cluster == i)[0])
#print(member_id[-1])
site.buff[member_id_index] = member_id
def cluster_list_k(self, k_list, data):
data = sp.vstack(data, format='csr')
# self.data = pd.DataFrame.sparse.from_spmatrix(v)
# data = pd.DataFrame(sp.vstack(data, format='csr').toarray())
# data = np.array(data)
self.k_index = 0
self.labels_list = []
self.RSS_list = []
self.centroids_list = []
ks = []
for k in k_list:
# print('\n')
print('\n' + str(k) + ":", end=" ")
# # try:
# labels, RSS, cents = KMeans.cluster_with_k(k, data)
# self.RSS_list.append(RSS)
# self.labels_list.append(np.array(labels))
# self.centroids_list.append(cents)
# ks.append(k)
# # except:
# # print('Failed', end='')
# try:
sk = KM(k, n_init=1, max_iter=30).fit(data)
self.labels_list.append(np.array(sk.labels_))
self.centroids_list.append(sk.cluster_centers_)
self.RSS_list.append(sk.inertia_)
ks.append(k)
# except:
# print('Failed', end='')
plt.plot(ks, self.RSS_list)
plt.savefig('./cluster_rss')
plt.show()
np.save("index" + file_post, [self.k_index])
np.save("label" + file_post, self.labels_list)
np.save("rss" + file_post, self.RSS_list)
np.save("cent" + file_post, self.centroids_list)
def em(tx, ty, rx, ry, reduced_data, add="", times=5, dataset="", alg=""):
clf = EM(n_components=times)
clf.fit(reduced_data)
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure()
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# centroids = clf.cluster_centers_
# plt.scatter(centroids[:, 0], centroids[:, 1],
# marker='x', s=169, linewidths=3,
# color='w', zorder=10)
plt.title(dataset + ': EM clustering (' + alg + '-reduced data)')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
clf = EM(n_components=times)
clf.fit(tx) #fit it to our data
test = clf.predict(tx)
result = clf.predict(rx)
checker = EM(n_components=times)
ry = ry.reshape(-1,1)
checker.fit(ry)
truth = checker.predict(ry)
td = np.reshape(test, (test.size, 1))
rd = np.reshape(result, (result.size, 1))
# newtx = np.append(td)
# newrx = np.append(rd)
myNN(test, ty, result, ry, alg="EM_"+alg)
errs = []
scores = []
# this is what we will compare to
checker = EM(n_components=2)
ry = ry.reshape(-1,1)
checker.fit(ry)
truth = checker.predict(ry)
adj_rand = []
v_meas = []
mutual_info = []
adj_mutual_info = []
# so we do this a bunch of times
for i in range(2,times):
clusters = {x:[] for x in range(i)}
# create a clusterer
clf = EM(n_components=i)
clf.fit(tx) #fit it to our data
test = clf.predict(tx)
result = clf.predict(rx) # and test it on the testing set
for index, val in enumerate(result):
clusters[val].append(index)
mapper = {x: round(sum(truth[v] for v in clusters[x])/float(len(clusters[x]))) if clusters[x] else 0 for x in range(i)}
processed = [mapper[val] for val in result]
errs.append(sum((processed-truth)**2) / float(len(ry)))
scores.append(clf.score(tx, ty))
adj_rand.append(metrics.adjusted_rand_score(ry.ravel(), result))
v_meas.append(metrics.v_measure_score(ry.ravel(), result))
mutual_info.append(metrics.fowlkes_mallows_score(ry.ravel(), result))
adj_mutual_info.append(metrics.homogeneity_score(ry.ravel(), result))
# plot([0, times, min(scores)-.1, max(scores)+.1],[range(2, times), scores, "-"], "Number of Clusters", "Log Likelihood", dataset+": EM Log Likelihood - " + alg, dataset+"_EM_"+alg)
# other metrics
# names = ["Adjusted Random", "V Measure", "Mutual Info", "Adjusted Mutual Info"]
plt.figure()
plt.title(dataset+": EM Clustering measures - "+alg)
plt.xlabel('Number of clusters')
plt.ylabel('Score value')
plt.plot(range(2,times),adj_rand, label="Adjusted Random")
plt.plot(range(2,times),v_meas, label="V Measure")
plt.plot(range(2,times),mutual_info, label = "Fowlkes Mallows Score")
plt.plot(range(2,times),adj_mutual_info, label="Homogeneity Score")
plt.legend()
plt.savefig("EMMetrics"+dataset+"_"+alg+".png")
kmeans = KM(n_clusters=2)
kmeans.fit(reduced_data)
Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Obtain labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
plt.title(dataset + ': EM clustering (' + alg + '-reduced data)\n'
'Centroids are marked with a white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
categories=categories,
shuffle=True,
random_state=None,
remove=('headers', 'footers'))
fp = open('Result/metrics.txt', 'w+')
# Question 1
data = dataset.data
target = dataset.target
data_matrix = tf_idf_matrix(data)
fp.write('tf_idf_matrix shape: {}\n'.format(data_matrix.shape))
# Question 2
target = [1 if i > 3 else 0 for i in target]
cluster = KM(n_clusters=2, random_state=0, max_iter=1000, n_init=30)
cluster.fit(data_matrix)
predict_target = cluster.labels_
fp.write("Contingency table: \n{}\n".format(
contingency_matrix(target, predict_target)))
# Question 3
metrics_dict = {
"homogeneity_score": homogeneity_score,
'completeness_score': completeness_score,
"v_measure_score": v_measure_score,
"adjusted_rand_score": adjusted_rand_score,
"adjusted_mutual_info_score": adjusted_mutual_info_score
}
for metrics_name in metrics_dict.keys():
# SCALE THE DATA
scaler = SS()
scaler.fit(df)
scaled_data = scaler.transform(df)
# PCA
pca = PCA(n_components=2)
pca.fit(scaled_data)
x_pca = pca.transform(scaled_data)
# K MEANS - MAKES A LOT OF SENSE TO APPLY THIS ON TOP OF PCA
from sklearn.cluster import KMeans as KM
model = KM(n_clusters=2)
model.fit(df)
fig, axes = plt.subplots(1, 2, figsize=(10, 6))
fig.suptitle('Breast Cancer', fontsize=20)
axes[0].set_title('Diagnosis')
axes[0].scatter(
x_pca[:, 0],
x_pca[:, 1],
c=model.labels_,
cmap='coolwarm',
)
axes[1].set_title('KMC')
axes[1].scatter(x_pca[:, 0], x_pca[:, 1], c=cancer['target'], cmap='coolwarm')
plt.show()
def km(tx, ty, rx, ry, reduced_data, add="", times=5, dataset="", alg=""):
processed = []
adj_rand = []
v_meas = []
mutual_info = []
adj_mutual_info = []
sil = []
inertia = []
for i in range(2,times):
clusters = {x:[] for x in range(i)}
clf = KM(n_clusters=i)
clf.fit(tx)
test = clf.predict(tx)
result = clf.predict(rx)
adj_rand.append(metrics.adjusted_rand_score(ry.ravel(), result))
v_meas.append(metrics.v_measure_score(ry.ravel(), result))
mutual_info.append(metrics.fowlkes_mallows_score(ry.ravel(), result))
adj_mutual_info.append(metrics.homogeneity_score(ry.ravel(), result))
inertia.append(clf.inertia_)
plots = [adj_rand, v_meas, mutual_info, adj_mutual_info]
plt.title(dataset+": KM Clustering measures - "+alg)
plt.xlabel('Number of clusters')
plt.ylabel('Score value')
plt.plot(range(2,times), adj_rand, label="Adjusted Random")
plt.plot(range(2,times), v_meas, label="V Measure")
plt.plot(range(2,times), mutual_info, label = "Fowlkes Mallows Score")
plt.plot(range(2,times), adj_mutual_info, label="Homogeneity Score")
plt.legend()
plt.ylim(ymin=-0.05, ymax=1.05)
plt.savefig("KMeansMetric"+dataset+"_"+alg+".png")
plt.figure()
plt.title(dataset+": KMeans Inertia - "+alg)
plt.xlabel('Number of clusters')
plt.ylabel('Inertia')
plt.plot(range(2,times), inertia)
plt.savefig("KM-Inertia-"+dataset+"-"+alg+".png")
td = np.reshape(test, (test.size, 1))
rd = np.reshape(result, (result.size, 1))
newtx = np.append(tx, td, 1)
newrx = np.append(rx, rd, 1)
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
best_clusterer = KM(n_clusters=4)
best_clusterer.fit(X)
Z = best_clusterer.predict(X)
print(len(Z))
print(len(X))
plt.figure(1)
plt.clf()
colors = ['r', 'g', 'b', 'y', 'c', 'm','#eeefff', '#317c15', '#4479b4', '#6b2b9c',
'#63133b', '#6c0d22', '#0c7c8c', '#67c50e','#c5670e', '#946c47', '#58902a', '#54b4e4',
'#e4549e', '#2b2e85' ]
for i in range(0, len(X)):
plt.plot(X[i][0], X[i][1], marker='.', color=colors[Z[i]], markersize=2)
#plt.plot(X[:, 0], X[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = best_clusterer.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='k', zorder=10)
plt.title('K-means Clusters ' + alg)
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
kmeans = KM(n_clusters=3)
kmeans.fit(tx)
result=pd.DataFrame(kmeans.transform(tx), columns=['KM%i' % i for i in range(3)])
my_color = pd.Series(ty).astype('category').cat.codes
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(result['KM0'], result['KM1'], result['KM2'], c=my_color, cmap="Dark2_r", s=60)
plt.show()
reduced_data = PCA(n_components=2).fit_transform(tx)
kmeans = KM(n_clusters=4)
kmeans.fit(reduced_data)
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
plt.figure()
plt.clf()
plt.imshow(Z, interpolation='nearest',
extent=(xx.min(), xx.max(), yy.min(), yy.max()),
cmap=plt.cm.Paired,
aspect='auto', origin='lower')
plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
marker='x', s=169, linewidths=3,
color='w', zorder=10)
plt.title(dataset + ': K-means clustering (' + alg + '-reduced data)\n'
'Centroids are marked with a white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.show()
checker = KM(n_clusters=2)
ry = ry.reshape(-1,1)
checker.fit(ry)
truth = checker.predict(ry)
clusters = {x:[] for x in range(4)}
clf = KM(n_clusters=4)
clf.fit(tx) #fit it to our data
test = clf.predict(tx)
result = clf.predict(rx) # and test it on the testing set
for index, val in enumerate(result):
clusters[val].append(index)
mapper = {x: round(sum(truth[v] for v in clusters[x])/float(len(clusters[x]))) if clusters[x] else 0 for x in range(4)}
processed = [mapper[val] for val in result]
print(sum((processed-truth)**2) / float(len(ry)))
clf = KM(n_clusters=times)
clf.fit(tx) #fit it to our data
test = clf.predict(tx)
result = clf.predict(rx)
checker = KM(n_clusters=times)
ry = ry.reshape(-1,1)
checker.fit(ry)
truth = checker.predict(ry)
td = np.reshape(test, (test.size, 1))
rd = np.reshape(result, (result.size, 1))
newtx = np.append(td)
newrx = np.append(rd)
myNN(test, ty, result, ry, alg="KM_"+alg)
nn(newtx, ty, newrx, ry, add="onKM"+add)
best_model = model
best_error = model.error
else:
if best_error > model.error:
best_error = model.error
best_model = model
label = best_model.predict(X)
center = best_model.center
####### plot ######################
plt.figure(1)
plt.subplot(121)
plt.title('my KMeans')
plt.scatter(X[:, 0], X[:, 1], c=label)
plt.scatter(center[:, 0], center[:, 1], c='r', marker='+')
####### compare to scikit ########
from sklearn.cluster import KMeans as KM
km = KM(n_clusters=k)
km.fit(X)
plt.subplot(122)
plt.title('scikit KMeans')
plt.scatter(X[:, 0], X[:, 1], c=km.labels_)
plt.scatter(km.cluster_centers_[:, 0],
km.cluster_centers_[:, 1],
c='r',
marker='+')
plt.show()
def __init__(self, imgPixels, K):
self.imgPixels = imgPixels
self.KM = KM(n_clusters=K, random_state=0).fit(self.imgPixels)
def kmtable(tx, ty, rx, ry, dataset=""):
processed = []
adj_rand = []
v_meas = []
mutual_info = []
adj_mutual_info = []
sil = []
inertia = []
compressor = PCA(n_components = tx[1].size/2)
compressor.fit(tx, y=ty)
pcatx = compressor.transform(tx)
pcarx = compressor.transform(rx)
p = []
compressor = ICA(n_components = tx[1].size/2)
compressor.fit(tx, y=ty)
icatx = compressor.transform(tx)
icarx = compressor.transform(rx)
ic = []
compressor = RandomProjection(tx[1].size/2)
compressor.fit(tx, y=ty)
rptx = compressor.transform(tx)
rprx = compressor.transform(rx)
r = []
compressor = best(k=tx[1].size/2)
compressor.fit(tx, y=ty)
kbtx = compressor.transform(tx)
kbrx = compressor.transform(rx)
k = []
for i in range(2,8):
# clusters = {x:[] for x in range(i)}
clf = KM(n_clusters=i)
clf.fit(pcatx)
test = clf.predict(pcatx)
result = clf.predict(pcarx)
p.append(metrics.v_measure_score(ry.ravel(), result))
clf = KM(n_clusters=i)
clf.fit(icatx)
test = clf.predict(icatx)
result = clf.predict(icarx)
ic.append(metrics.v_measure_score(ry.ravel(), result))
clf = KM(n_clusters=i)
clf.fit(rptx)
test = clf.predict(rptx)
result = clf.predict(rprx)
r.append(metrics.v_measure_score(ry.ravel(), result))
clf = KM(n_clusters=i)
clf.fit(kbtx)
test = clf.predict(kbtx)
result = clf.predict(kbrx)
k.append(metrics.v_measure_score(ry.ravel(), result))
# adj_rand.append(metrics.adjusted_rand_score(ry.ravel(), result))
# v_meas.append(metrics.v_measure_score(ry.ravel(), result))
# mutual_info.append(metrics.fowlkes_mallows_score(ry.ravel(), result))
# adj_mutual_info.append(metrics.homogeneity_score(ry.ravel(), result))
plt.figure()
plt.title(dataset+": KM Clustering & DR")
plt.xlabel('Number of clusters')
plt.ylabel('V Measure Score value')
plt.plot(range(2,8), p, label="PCA")
plt.plot(range(2,8), ic, label="ICA")
plt.plot(range(2,8), r, label = "RP")
plt.plot(range(2,8), k, label="KB")
plt.legend()
plt.ylim(ymin=-0.05, ymax=0.5)
plt.savefig("KM_DR_"+dataset+"_VM.png", dpi=300)
lower conversion price and more dilution to BankAmerica stock
holders, noted Daniel Williams, analyst with Sutro Group.
Several analysts said that while they believe the Brazilian
debt problem will continue to hang over the banking industry
through the quarter, the initial shock reaction is likely to
ease over the coming weeks.
Nevertheless, BankAmerica, which holds about 2.70 billion
dlrs in Brazilian loans, stands to lose 15-20 mln dlrs if the
interest rate is reduced on the debt, and as much as 200 mln
dlrs if Brazil pays no interest for a year, said Joseph
Arsenio, analyst with Birr, Wilson and Co.
He noted, however, that any potential losses would not show
up in the current quarter.
'''
kmeans = KM(n_clusters=32, init='random', n_init=1, verbose=1)
kmeans.fit(features)
print kmeans.labels_
new_post_vector = vectorizer.transform([new_post_2])
new_post_label = kmeans.predict(new_post_vector)[0]
print "new posts label", new_post_label
similar_indices = (kmeans.labels_ == new_post_label).nonzero()[0]
similar = []
for i in similar_indices:
dist = sp.linalg.norm((new_post_vector - features[i]).toarray())
if __name__ == '__main__':
dir_from = '../drosophila_kc167_1_images'
dir_to = '../KMeansResults'
number_clusters = [i for i in range(2, 70, 2)] #[2, 4, 6, 8, 10, 12, 14, 16]
for file_name in os.listdir(dir_from):
array_time = []
path_file = dir_from + '/' + file_name
name_without_extension = file_name[:-4]
dir_save = dir_to + '/' + name_without_extension + '/'
# os.mkdir(dir_save)
data = KMeans(path_file).pixels
sse = []
for K in number_clusters:
path_file = '../drosophila_kc167_1_images/CPvalid1_48_40x_Tiles_p0003DAPI.TIF'
#label, center = KMeans(path_file, K).kmeans()
km = KM(K)
km.fit(data)
sse.append(km.inertia_)
# Plot sse against k
plt.figure(figsize=(6, 6))
plt.plot(number_clusters, sse, '-o')
plt.xlabel(r'Number of clusters *k*')
plt.ylabel('Sum of squared distance');
plt.show()
exit()
# center = np.uint8(center)
# res = center[label].reshape((512, 512, 3))
# Image.fromarray(res, 'RGB').save(dir_save + str(K) + '.bmp')
X_train_people_nmf = nmf_decomp.transform(X_train_people)
X_test_people_nmf = nmf_decomp.transform(X_test_people)
nmf_decomp = NMF(n_components=5, init='nndsvd',
random_state=37).fit(X_train_energy)
X_train_energy_nmf = nmf_decomp.transform(X_train_energy)
X_test_energy_nmf = nmf_decomp.transform(X_test_energy)
nmf_decomp = NMF(n_components=100, random_state=37).fit(X_train_mnist)
X_train_mnist_nmf = nmf_decomp.transform(X_train_mnist)
X_test_mnist_nmf = nmf_decomp.transform(X_test_mnist)
print('nmf')
#k-Means Clustering
from sklearn.cluster import KMeans as KM
km_cluster = KM(n_clusters=10, random_state=37).fit(X_train_people)
X_train_people_km = km_cluster.transform(X_train_people)
X_test_people_km = km_cluster.transform(X_test_people)
km_cluster = KM(n_clusters=10, n_init=5, random_state=37).fit(X_train_energy)
X_train_energy_km = km_cluster.transform(X_train_energy)
X_test_energy_km = km_cluster.transform(X_test_energy)
km_cluster = KM(n_clusters=10, random_state=37).fit(X_train_mnist)
X_train_mnist_km = km_cluster.transform(X_train_mnist)
X_test_mnist_km = km_cluster.transform(X_test_mnist)
print('km\n')
"""
##############################Classification##############################
"""
from sklearn.neighbors import KNeighborsClassifier as KNC
def __init__(self):
self.ma1 = TF.fit_transform(ma)
self.model = KM(6)
self.res = self.model.fit_predict(self.ma1)
#先随机在数据中扔几个点，作为核心。再计算每一个点距离核心的位置。
#每个核心肯定会有边境点，之后计算每个核心分离的中间位置，把核心偏移过去。重新形成新的边境
#多次循环后核心就会稳定
import pandas as pda
import numpy as np
from sklearn.cluster import KMeans as KM
if __name__ == '__main__':
data = pda.read_csv('luqu.csv')
x = data.iloc[:, 1:4].as_matrix()
km = KM(n_clusters=2, n_jobs=2)
print(km.fit_predict(x))
for key, name in webcolors.css21_hex_to_names.items():
r_c, g_c, b_c = webcolors.hex_to_rgb(key)
rd = (r_c - rgb_[0])**2
gd = (g_c - rgb_[1])**2
bd = (b_c - rgb_[2])**2
min_colors[(rd + gd + bd)] = name
return min_colors[min(min_colors.keys())]
# Construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("--image", required=True, help="Input Image Path")
ap.add_argument("--clusters", required=True, type=int, help="# of clusters")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Reshape the image to be a list of pixels
image = image.reshape((image.shape[0] * image.shape[1], 3))
# K-Means Clustering
clt = KM(n_clusters=args["clusters"])
clt.fit(image)
# Get dominant colors
colors = clt.cluster_centers_.astype("uint8").tolist()
for rgb in colors:
color_name = get_color_name(rgb)
print("Dominant color :", color_name)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# One hot encode target values
one_hot = OneHotEncoder()
y_train_hot = one_hot.fit_transform(y_train.reshape(-1, 1)).todense()
y_test_hot = one_hot.transform(y_test.reshape(-1, 1)).todense()
labels = y_train
dim = [2, 3, 4, 5]
km = KM(random_state=42)
gmm = GMM(random_state=42)
Score = defaultdict(list)
adjMI = defaultdict(list)
S_homog = defaultdict(list)
S_adjMI = defaultdict(list)
S_vm = defaultdict(list)
for i in dim:
reduced_X = PCA(n_components=i,
random_state=42).fit_transform(X_train_scaled)
k = 30
km.set_params(n_clusters=k)
gmm.set_params(n_components=k)
km.fit(reduced_X)
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans as KM
file = pd.read_csv("Mall_Customers.csv")
X = file.iloc[:, 3:5].values
elbow = []
for i in range(10):
km = KM(n_clusters=i + 1)
km.fit(X)
elbow.append(km.inertia_)
plt.plot(range(1, 11), elbow)
plt.xlabel("No. of Clusters")
plt.ylabel("Cost")
plt.show()
km = KM(n_clusters=5)
res = km.fit_predict(X)
colors = ["red", "blue", "green", "yellow", "silver"]
for i in range(5):
plt.scatter(X[res == i, 0], X[res == i, 1], c=colors[i])
plt.axes().get_xaxis().set_visible(False)
plt.axes().get_yaxis().set_visible(False)
plt.show()
