def use_k_means(x_pca, y):
k = 10
y_pred, centroids_pred = k_mean(k, x_pca)
score = get_score(y, y_pred)
print(score)
title = 'k-means-mnist-socre-' + str(score)
k_show(x_pca, title, y_pred, centroids_pred)
def k_mean_validation(K):
D = 2
B = 10000
KM = km.k_mean("data2D.npy")
# Required argument: numbers of clusters, dimensions of points, numbers of points
_, segment_ids, X_data, mu = KM.cluster(K, D, B, 1.0 / 3.0)
# Take the validation set as input to calculate the loss from cluster centers
loss, _ = KM.cal_loss(KM.validation.astype(np.float32), mu, D)
with tf.Session():
print "K =", K, ":", loss.eval()
def k_mean_validation(K):
D = 2
B = 10000
KM = km.k_mean("data2D.npy")
# Required argument: numbers of clusters, dimensions of points, numbers of points
_, segment_ids, X_data, mu= KM.cluster(K, D, B, 1.0/3.0)
# Take the validation set as input to calculate the loss from cluster centers
loss,_ = KM.cal_loss(KM.validation.astype(np.float32), mu, D)
with tf.Session():
print "K =",K,":",loss.eval()
def k_comparison(K):
D = 2
B = 10000
KM = km.k_mean("data2D.npy")
_, segment_ids, X_data, mu= KM.cluster(K, D, B)
data = tf.ones(shape = [B,])
division = tf.unsorted_segment_sum(data, segment_ids, K, name=None)
with tf.Session():
print "K =",K,":",division.eval()/10000
plot.plot_cluster(segment_ids, X_data, mu, K)
def k_comparison(K):
D = 2
B = 10000
KM = km.k_mean("data2D.npy")
_, segment_ids, X_data, mu= KM.cluster(K, D, B)
data = tf.ones(shape = [B,])
division = tf.unsorted_segment_sum(data, segment_ids, K, name=None)
with tf.Session():
print "K =",K,":",division.eval()/10000
plot.plot_cluster(segment_ids, X_data, mu, K)
def cluster_articles(supervised=False,
target_dimension=10,
sentence=False,
path_result="result.csv"):
""" Cluster the articles with a k_mean algorithms, writes the results
in a csv file and return a list of cluster objects"""
chemin = "preprocessed_df" + sentence * "_sentence" + ".csv"
try:
# Load a preprocessed df
df = pd.read_csv(chemin, index_col=0)
except IOError:
# Build the preprecessed df if it is missing
print("Missing preprocessed file")
df = create_df("df_brown.csv", sentence=sentence)
df.to_csv(chemin)
if supervised:
real_clusters = df["real_cluster"]
df = df.drop("real_cluster", axis=1)
df = df.drop("text", axis=1)
if PCA_val:
# Reduce the dimension with a PCA
pca = PCA(n_components=target_dimension, svd_solver='full')
print("Fit and Transform...")
pca.fit(df)
print("Transform...")
df_reduced = pd.DataFrame(pca.transform(df))
if MDS_val:
# Reduce the dimension with a MDS
mds = manifold.MDS(target_dimension, max_iter=100, n_init=1)
df_reduced = pd.DataFrame(mds.fit_transform(df))
# Del the initial df
del df
# The score at each iteration of the algorithm
scores = []
model = None
centroids = None
for i in range(N):
if centroids:
# Centroids already intialized
clusters, cost, centroids = k_mean(df_reduced,
number_clusters,
centroids=centroids)
labels = [value for (key, value) in sorted(clusters.items())]
score = silhouette_score(df_reduced, labels, metric='cosine')
if not centroids:
best_score = 0
# First iterations, we run the algorithm mutliple times and look for the best centroids
for _ in range(num_first_tests):
clusters, cost, centroids = k_mean(df_reduced, number_clusters)
labels = [value for (key, value) in sorted(clusters.items())]
score = silhouette_score(df_reduced, labels, metric='cosine')
if score > best_score:
# Best iteratoin, we save the configuration
best_centroids = centroids
best_score = score
best_labels = labels
best_clusters = clusters
centroids = best_centroids
score = best_score
labels = best_labels
clusters = best_clusters
scores += [score]
# Pairwise distances between every points
distances = pairwise_distances(df_reduced, metric='cosine')
# Silhouette score for each point
sil_samples = silhouette_samples(distances, labels, metric='cosine')
# Find the the misclassified elements
min_elements = choose_min_elements(sil_samples)
# Load the inital dataset and split in training and testing set
df_brown = pd.DataFrame.from_csv("df_brown.csv")
df_test_brown = df_brown.ix[min_elements]
df_train_brown = df_brown.drop(df_brown.index[min_elements])
labels_as_dict = {}
for a in range(len(labels)):
if a not in min_elements:
labels_as_dict[a] = labels[a]
train_Y = pd.DataFrame(labels_as_dict.values())
labels = pd.get_dummies(train_Y[0])
# number of clusters in the df
s = labels.shape[1]
# Correction when a clusters is deleted by the algorithm
if s < number_clusters:
headers = list(labels)
size_train = df_train_brown.shape[0]
for i in range(number_clusters):
if i not in headers:
labels[i] = np.array([0] * size_train)
train_Y.to_csv("train_y_nn.csv")
df_train_brown.to_csv("train_nn.csv")
df_test_brown.to_csv("test_nn.csv")
# Neural network returns prediction, new vector and model
predictions, new_vectors, model = predict_neural_network(
df_train_brown, labels, df_test_brown, target_dimension,
number_clusters, model)
# Update the vectors in the dataframe
for j in range(len(min_elements)):
index = min_elements[j]
vector = new_vectors[j]
for k in range(len(vector)):
df_reduced[k][index] = vector[k]
# Write the results in a csv file
predicted_clusters = [value for (key, value) in sorted(clusters.items())]
df_brown["pred_cluster"] = predicted_clusters
df_brown.to_csv(path_result)
# Uncomment to plot the silhouette according to the iterations
"""
plt.xlabel("Number of clusters")
plt.ylabel("Silhouette")
plt.title("Evolution of the silhouette after " + str(N) + " iterations")
plt.plot(range(N), scores)
plt.show()
"""
return clusters, df_reduced
def main():
input=input_pipeline()
print("ready input pipeline")
net=k_mean()
_solver=solver(net,input,'./log')
_solver.train_and_test()
i_time = config.getint('sys','i_time')
with open(complete_file_path,'w') as f:
f.write("0,"+str(i_time * 11))
with open(os.path.join(file_path,"pid.txt"),'w') as f:
f.write(str(os.getpid()))
from knn import knn
from ada_boost import ada_boost
from random_forest import random_forest
from logistic import logistic
from svm import svm
from decision_tree import c4_5,cart
from k_mean import k_mean
from xgboost_clf import xgboost
from gbdt_clf import gbdt
from net import net
print('knn:',knn(i_time=i_time))
print('AdaBoost:',ada_boost(i_time=i_time))
print('random forest',random_forest(i_time=i_time))
print('logistic regression:',logistic(i_time=i_time))
print('C4.5:',c4_5(i_time=i_time))
print('cart:',cart(i_time=i_time))
print('k_mean',k_mean(i_time=i_time))
print('xgboost',xgboost(i_time=i_time))
print('gbdt',gbdt(i_time=i_time))
print('SVM:',svm(i_time=i_time))
print('net',net(i_time=i_time))
ratio = random.random()
raw_cubs.append(
Lion(selected_male.x * ratio + selected_female.x *
(1 - ratio)))
raw_cubs.append(
Lion(selected_female.x * ratio + selected_male.x *
(1 - ratio)))
#選擇幾隻突變
selected_mutation_cubs = random.sample(
raw_cubs, int(len(raw_cubs) * mutation_ratio))
for i in selected_mutation_cubs:
i.mutation(maxvalue=y, minvalue=x)
#k_mean分群
common_gender_cubs = k_mean.k_mean(raw_cubs, 2, 10)
boy_cubs = common_gender_cubs[0]
girl_cubs = common_gender_cubs[1]
#刪除多餘弱小的子代
diff = len(boy_cubs) - len(girl_cubs)
if diff > 0:
for i in range(diff):
boy_cubs.remove(min(boy_cubs, key=attrgetter('fitness')))
else:
for i in range(-diff):
girl_cubs.remove(min(girl_cubs, key=attrgetter('fitness')))
#領土防禦
for meow in range(generate_nomad_time): #產生流浪獅子
nomads.append(Lion(random.uniform(x, y)))
return(dis)
def cost(m,data):
cost=0
for i in range(len(data)):
dist=euclidean_distance(m,data[i])
cost=cost+dist
return(cost)
# k_mean......for k=2...............
kn=4
cluster=[[] for x in range(kn)]
mean= [[] for x in range(kn)]
i=0
k=0
cluster[k],cluster[k+1],mean[k],mean[k+1] = k_mean(my_data)
while(1):
cost_dist=[]
for i in range(k+1):
cost1=cost(mean[i],cluster[i])
cost_dist.append(cost1)
a=cost_dist.index(max(cost_dist))
cluster[a],cluster[k+2],mean[a],mean[k+2]=k_mean(cluster[a])
k=k+1
if (kn==k+2):
break