'''
使用Iris数据集（鸢尾花卉数据集）来进行我们的第一次预测。
该数据集包含150条记录的一组数据，有5个属性——花瓣长度，花瓣宽度，萼片长度，萼片宽度和类别。
三个类别分别是Iris Setosa（山鸢尾），Iris Virginica（维吉尼亚鸢尾）和Iris Versicolor（变色鸢尾）。
对于我们的无监督算法，我们给出鸢尾花的这四个特征，并预测它属于哪一类。
'''
# Importing Modules
from sklearn import datasets
import matplotlib.pyplot as plt# Loading dataset
iris_df = datasets.load_iris()
print(dir(iris_df))# Features
print(iris_df.feature_names)# Targets
print(iris_df.target)# Target Names
print(iris_df.target_names)
label = {0:'red',1:'blue',2:'green'}# Dataset slicing
x_axis = iris_df.data[:,0]
y_axis = iris_df.data[:,2]# Plotting
plt.scatter(x_axis, y_axis, c=iris_df.target)
plt.show()

# Importing Modules
from sklearn import datasets
from sklearn.cluster import KMeans# Loaning dataset
iris_df = datasets.load_iris()# Declaring Model 初始化模型
model = KMeans(n_clusters=3)# Fitting Model
model.fit(iris_df.data)# Predicting a single input
predicted_label = model.predict([[7.2, 3.5, 0.8, 1.6]])# Prediction on the entire data
all_predictions = model.predict(iris_df.data)# Printing Predictions
print(predicted_label)
print(all_predictions)

from scipy.cluster.hierarchy import linkage, dendrogram
import matplotlib.pyplot as plt
import pandas as pdseeds_df = pd.read_csv("https://raw.githubusercontent.com/vihar/unsupervised-learning-with-python/master/seeds-less-rows.csv")# remove the grain species from the DataFrame, save for later
varieties = list(seeds_df.pop('grain_variety'))# extract the measurements as a NumPy array
samples = seeds_df.values"""
Perform hierarchical clustering on samples using the
linkage() function with the method='complete' keyword argument.
Assign the result to mergings.
"""
mergings = linkage(samples, method='complete')"""
Plot a dendrogram using the dendrogram() function on mergings,
specifying the keyword arguments labels=varieties, leaf_rotation=90,
and leaf_font_size=6.
"""dendrogram(mergings,labels=varieties,leaf_rotation=90,leaf_font_size=6,)plt.show()

# Importing Modules
from sklearn import datasets
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt# Loading dataset
iris_df = datasets.load_iris()# Defining Model
model = TSNE(learning_rate=100)# Fitting Model
transformed = model.fit_transform(iris_df.data)# Plotting 2d t-Sne
x_axis = transformed[:, 0]
y_axis = transformed[:, 1]plt.scatter(x_axis, y_axis, c=iris_df.target)
plt.show()'''
这里Iris数据集具有四个特征（4d），它被变换并以二维图形表示。类似地，t-SNE模型可以应用于具有n个特征的数据集
'''

# Importing Modules
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA# Load Dataset
iris = load_iris()# Declaring Model
dbscan = DBSCAN()# Fitting
dbscan.fit(iris.data)# Transoring Using PCA
pca = PCA(n_components=2).fit(iris.data)
pca_2d = pca.transform(iris.data)# Plot based on Class
for i in range(0, pca_2d.shape[0]):if dbscan.labels_[i] == 0:c1 = plt.scatter(pca_2d[i, 0], pca_2d[i, 1], c='r', marker='+')elif dbscan.labels_[i] == 1:c2 = plt.scatter(pca_2d[i, 0], pca_2d[i, 1], c='g', marker='o')elif dbscan.labels_[i] == -1:c3 = plt.scatter(pca_2d[i, 0], pca_2d[i, 1], c='b', marker='*')plt.legend([c1, c2, c3], ['Cluster 1', 'Cluster 2', 'Noise'])
plt.title('DBSCAN finds 2 clusters and Noise')
plt.show()

'''Comparison of the K-Means and MiniBatchKMeans clustering algorithms'''print(__doc__)import timeimport numpy as np
import matplotlib.pyplot as pltfrom sklearn.cluster import MiniBatchKMeans, KMeans
from sklearn.metrics.pairwise import pairwise_distances_argmin
from sklearn.datasets.samples_generator import make_blobs# #############################################################################
# Generate sample data
np.random.seed(0)batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
n_clusters = len(centers)
X, labels_true = make_blobs(n_samples=300000, centers=centers, cluster_std=0.7)# #############################################################################
# Compute clustering with Meansk_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
t0 = time.time()
k_means.fit(X)
t_batch = time.time() - t0# #############################################################################
# Compute clustering with MiniBatchKMeansmbk = MiniBatchKMeans(init='k-means++', n_clusters=3, batch_size=batch_size,n_init=10, max_no_improvement=10, verbose=0)
t0 = time.time()
mbk.fit(X)
t_mini_batch = time.time() - t0# #############################################################################
# Plot resultfig = plt.figure(figsize=(8, 3))
fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.9)
colors = ['#4EACC5', '#FF9C34', '#4E9A06']# We want to have the same colors for the same cluster from the
# MiniBatchKMeans and the KMeans algorithm. Let's pair the cluster centers per
# closest one.
k_means_cluster_centers = np.sort(k_means.cluster_centers_, axis=0)
mbk_means_cluster_centers = np.sort(mbk.cluster_centers_, axis=0)
k_means_labels = pairwise_distances_argmin(X, k_means_cluster_centers)
mbk_means_labels = pairwise_distances_argmin(X, mbk_means_cluster_centers)
order = pairwise_distances_argmin(k_means_cluster_centers,mbk_means_cluster_centers)# KMeans
ax = fig.add_subplot(1, 3, 1)
for k, col in zip(range(n_clusters), colors):my_members = k_means_labels == kcluster_center = k_means_cluster_centers[k]ax.plot(X[my_members, 0], X[my_members, 1], 'w',markerfacecolor=col, marker='.')ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,markeredgecolor='k', markersize=6)
ax.set_title('KMeans')
ax.set_xticks(())
ax.set_yticks(())
plt.text(-3.5, 1.8,  'train time: %.2fs\ninertia: %f' % (t_batch, k_means.inertia_))# MiniBatchKMeans
ax = fig.add_subplot(1, 3, 2)
for k, col in zip(range(n_clusters), colors):my_members = mbk_means_labels == order[k]cluster_center = mbk_means_cluster_centers[order[k]]ax.plot(X[my_members, 0], X[my_members, 1], 'w',markerfacecolor=col, marker='.')ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,markeredgecolor='k', markersize=6)
ax.set_title('MiniBatchKMeans')
ax.set_xticks(())
ax.set_yticks(())
plt.text(-3.5, 1.8, 'train time: %.2fs\ninertia: %f' %(t_mini_batch, mbk.inertia_))# Initialise the different array to all False
different = (mbk_means_labels == 4)
ax = fig.add_subplot(1, 3, 3)for k in range(n_clusters):different += ((k_means_labels == k) != (mbk_means_labels == order[k]))identic = np.logical_not(different)
ax.plot(X[identic, 0], X[identic, 1], 'w',markerfacecolor='#bbbbbb', marker='.')
ax.plot(X[different, 0], X[different, 1], 'w',markerfacecolor='m', marker='.')
ax.set_title('Difference')
ax.set_xticks(())
ax.set_yticks(())plt.show()

# Demo of affinity propagation clustering algorithm
print(__doc__)from sklearn.cluster import AffinityPropagation
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs# #############################################################################
# Generate sample data
centers = [[1, 1], [-1, -1], [1, -1]]
X, labels_true = make_blobs(n_samples=300, centers=centers, cluster_std=0.5,random_state=0)# #############################################################################
# Compute Affinity Propagation
af = AffinityPropagation(preference=-50).fit(X)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_n_clusters_ = len(cluster_centers_indices)print('Estimated number of clusters: %d' % n_clusters_)
print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels_true, labels))
print("Completeness: %0.3f" % metrics.completeness_score(labels_true, labels))
print("V-measure: %0.3f" % metrics.v_measure_score(labels_true, labels))
print("Adjusted Rand Index: %0.3f"% metrics.adjusted_rand_score(labels_true, labels))
print("Adjusted Mutual Information: %0.3f"% metrics.adjusted_mutual_info_score(labels_true, labels))
print("Silhouette Coefficient: %0.3f"% metrics.silhouette_score(X, labels, metric='sqeuclidean'))# #############################################################################
# Plot result
import matplotlib.pyplot as plt
from itertools import cycleplt.close('all')
plt.figure(1)
plt.clf()colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')
for k, col in zip(range(n_clusters_), colors):class_members = labels == kcluster_center = X[cluster_centers_indices[k]]plt.plot(X[class_members, 0], X[class_members, 1], col + '.')plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,markeredgecolor='k', markersize=14)for x in X[class_members]:plt.plot([cluster_center[0], x[0]], [cluster_center[1], x[1]], col)plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()