def main(readfile):
X = np.loadtxt(readfile)
centers = sc.search_center(X, 10, 0.5, 0.1, distance, True)
labels = ["blue" for i in range(len(X))]
C = np.zeros((0, 2))
for c in centers:
labels[c[0]] = "red"
C = np.r_[C, np.array(X[c[0]]).reshape(1, -1)]
r = li.get_num_of_clusters(C, False)
model = gm.GaussianMixture(len(r))
model.fit(C, iter_max=100)
labels = model.classify(C)
plt.scatter(X[:, 0], X[:, 1], s=5)
x_test, y_test = np.meshgrid(np.linspace(-10, 10, 100),
np.linspace(-10, 10, 100))
probs = model.predict_proba(
np.array([x_test, y_test]).reshape(2, -1).transpose())
plt.contour(x_test,
y_test,
probs.reshape(100, 100),
levels=[i for i in np.arange(0.0, 1.0, 0.01)])
plt.xlim(-10, 10)
plt.ylim(-10, 10)
#plt.savefig("output.png")
plt.show()
def main(filename):
X = np.loadtxt(filename)
model = gm.GaussianMixture(1)
model.fit(X, iter_max=100)
labels = model.classify(X)
plt.scatter(X[:, 0], X[:, 1], c=["blue" for label in labels], s=5)
x_test, y_test = np.meshgrid(np.linspace(-10, 10, 100), np.linspace(-10, 10, 100))
probs = model.predict_proba(np.array([x_test, y_test]).reshape(2, -1).transpose())
plt.contour(x_test, y_test, probs.reshape(100, 100), levels=[i for i in np.arange(0.0, 1.0, 0.01)])
plt.xlim(-10, 10)
plt.ylim(-10, 10)
#plt.savefig("output.png")
plt.show()
def main(filename):
X = np.loadtxt(filename)
model = gm.GaussianMixture(3)
model.fit(X, iter_max=100)
labels = model.classify(X)
colors = ["red", "blue", "green"]
plt.scatter(X[:, 0],
X[:, 1],
c=[colors[int(label)] for label in labels],
s=5)
plt.xlim(-10, 10)
plt.ylim(-10, 10)
#plt.savefig("output.png")
plt.show()