In [18]:
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
%matplotlib inline
from sklearn import datasets
#Iris Dataset
iris = datasets.load_iris()
X = iris.data
#KMeans
km = KMeans(n_clusters=100)
km.fit(X)
km.predict(X)
labels = km.labels_
#Plotting
fig = plt.figure(1, figsize=(7,7))
ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)
for spine in ax.spines.values(): spine.set_visible(False)
ax.scatter(X[:, 3], X[:, 0], X[:, 2],
c=labels.astype(np.float), edgecolor="k", s=50)
ax.set_xlabel("Petal width")
ax.set_ylabel("Sepal length")
ax.set_zlabel("Petal length")
plt.title("K Means", fontsize=14)
Out[18]:
In [17]:
X.shape
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In [3]:
from sklearn.mixture import GaussianMixture
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
%matplotlib inline
from sklearn import datasets
#Iris Dataset
iris = datasets.load_iris()
X = iris.data
#Gaussian Mixture Model
gmm = GaussianMixture(n_components=3)
gmm.fit(X)
proba_lists = gmm.predict_proba(X)
#Plotting
colored_arrays = np.matrix(proba_lists)
colored_tuples = [tuple(i.tolist()[0]) for i in colored_arrays]
fig = plt.figure(1, figsize=(7,7))
ax = Axes3D(fig, rect=[0, 0, 0.95, 1], elev=48, azim=134)
for spine in ax.spines.values(): spine.set_visible(False)
ax.scatter(X[:, 3], X[:, 0], X[:, 2],
c=colored_tuples, edgecolor="k", s=50)
ax.set_xlabel("Petal width")
ax.set_ylabel("Sepal length")
ax.set_zlabel("Petal length")
plt.title("Gaussian Mixture Model", fontsize=14)
Out[3]:
In [19]:
from hmmlearn import hmm
import numpy as np
%matplotlib inline
from sklearn import datasets
#Data
boston = datasets.load_boston()
ts_data = boston.data[1,:]
#HMM Model
gm = hmm.GaussianHMM(n_components=3)
gm.fit(ts_data.reshape(-1, 1))
states = gm.predict(ts_data.reshape(-1, 1))
#Plot
color_dict = {0:"r",1:"g",2:"b"}
color_array = [color_dict[i] for i in states]
plt.scatter(range(len(ts_data)), ts_data, c=color_array)
plt.title("HMM Model")
Out[19]: