def robust_example(plot=False):
X, y = load_robust()
clusters = 5
k = KMeans(K=clusters, max_iters=50, init="++")
k.fit(X)
k.predict()
if plot:
k.plot()
def kmeans_example(plot=False):
X, y = make_blobs(centers=4, n_samples=500, n_features=2,
shuffle=True, random_state=42)
clusters = len(np.unique(y))
k = KMeans(K=clusters, max_iters=150, init='++')
k.fit(X)
k.predict()
if plot:
k.plot()
def iris_example(plot=False):
X, y = load_iris()
clusters = len(np.unique(y))
k = KMeans(K=clusters, max_iters=50, init="++")
k.fit(X, y)
k.predict()
data = np.zeros([k.n_samples, 2])
# Dimension reducing
# Sepal width*length
data[:, 0] = k.X[:, 0] * k.X[:, 1]
# Petal width*length
data[:, 1] = k.X[:, 2] * k.X[:, 3]
if plot:
k.plot(data)
def KMeans_and_GMM(K):
COLOR = 'bgrcmyk'
X, y = make_clusters(skew=True, n_samples=1500, centers=K)
_, axes = plt.subplots(1, 3)
# Ground Truth
axes[0].scatter(X[:, 0], X[:, 1], c=[COLOR[int(assignment)] for assignment in y])
axes[0].set_title("Ground Truth")
# KMeans
kmeans = KMeans(K=K, init='++')
kmeans.fit(X)
kmeans.predict()
axes[1].set_title("KMeans")
kmeans.plot(ax=axes[1], holdon=True)
# Gaussian Mixture
gmm = GaussianMixture(K=K, init='kmeans')
gmm.fit(X)
axes[2].set_title("Gaussian Mixture")
gmm.plot(ax=axes[2])
def KMeans_and_GMM(K):
COLOR = 'bgrcmyk'
X, y = make_clusters(skew=True, n_samples=1500, centers=K)
_, axes = plt.subplots(1, 3)
# Ground Truth
axes[0].scatter(X[:, 0], X[:, 1], c=[COLOR[int(assignment)] for assignment in y])
axes[0].set_title("Ground Truth")
# KMeans
kmeans = KMeans(K=K, init='++')
kmeans.fit(X)
kmeans.predict()
axes[1].set_title("KMeans")
kmeans.plot(ax=axes[1], holdon=True)
# Gaussian Mixture
gmm = GaussianMixture(K=K, init='kmeans')
gmm.fit(X)
axes[2].set_title("Gaussian Mixture")
gmm.plot(ax=axes[2])
