def gauss_dimensions_mean(dimensions=range(2, 100, 20),
total_points=200,
num_experiments=100,
d=None):
# data distribution
k = 2
delta = 0.7
if not d:
d = dimensions[0]
table = np.zeros((num_experiments * len(dimensions), 6))
count = 0
for D in dimensions:
for i in range(num_experiments):
### generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((delta * np.ones(d), np.zeros(D - d)))
s2 = np.eye(D)
# flip Bernoulli coins to get number of points in each cluster
n1, n2 = np.random.multinomial(total_points, [0.5, 0.5])
# get data, construct gram Matrix
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x - y)
G = eclust.kernel_matrix(X, rho)
##################
### cluster with different algorithms
# can change number of times we execute each experiment
# and initialization as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 3] = run_clustering.spectral(k, X, G, z, run_times=5)
table[count, 4] = run_clustering.kmeans(k,
X,
z,
init="k-means++",
run_times=5)
table[count, 5] = run_clustering.gmm(k,
X,
z,
init="kmeans",
run_times=5)
####################
count += 1
return table
def cigars_circles(num_experiments=10, run_times=5, kind='cigars'):
table = []
for i in range(num_experiments):
this_experiment = []
if kind == 'cigars':
m1 = [0,0]
m2 = [6.5,0]
s1 = np.array([[1,0],[0,20]])
s2 = np.array([[1,0],[0,20]])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [200, 200])
k = 2
init = 'k-means++'
elif kind == '2circles':
X, z = data.circles([1, 3], [0.2, 0.2], [400, 400])
k = 2
init = 'random'
elif kind == '3circles':
X, z = data.circles([1, 3, 5], [0.2, 0.2, 0.2], [400, 400, 400])
init = 'random'
k = 3
else:
raise ValueError("Don't know which example to sample.")
#sigma = 2
sigma = 1
G = eclust.kernel_matrix(X, rho_standard)
G_half = eclust.kernel_matrix(X, rho_half)
G_exp = eclust.kernel_matrix(X, lambda x,y: rho_exp(x, y, sigma))
G_rbf = eclust.kernel_matrix(X, lambda x,y: rho_rbf(x, y, sigma))
this_experiment.append(
run_clustering.energy_hartigan(k,X,G,z,init=init,
run_times=run_times))
this_experiment.append(
run_clustering.energy_hartigan(k,X,G_half,z,
init=init,run_times=run_times))
this_experiment.append(
run_clustering.energy_hartigan(k,X,G_exp,z,
init=init,run_times=run_times))
this_experiment.append(
run_clustering.energy_hartigan(k,X,G_rbf,z,
init=init,run_times=run_times))
#this_experiment.append(
# run_clustering.spectral(k,X,G_exp,z,run_times=run_times))
this_experiment.append(
run_clustering.spectral(k,X,G_rbf,z,run_times=run_times))
this_experiment.append(
run_clustering.kmeans(k,X,z,init=init,run_times=run_times))
this_experiment.append(
run_clustering.gmm(k,X,z,init="kmeans",run_times=run_times))
this_experiment.append(energy.metric.accuracy(z, energy.gmm.gmm(k,X)))
table.append(this_experiment)
table = np.array(table)
for i in range(8):
print table[:,i].mean(), scipy.stats.sem(table[:,i])
def gauss_dimensions_cov(
dimensions=range(2, 100, 20), total_points=200, num_experiments=100,
d=10):
"""High dimensions but with nontrivial covariance."""
k = 2
if not d:
d = dimensions[0]
table = np.zeros((num_experiments * len(dimensions), 6))
count = 0
for D in dimensions:
for l in range(num_experiments):
# generate data
m1 = np.zeros(D)
m2 = np.concatenate((np.ones(d), np.zeros(D - d)))
s1 = np.eye(D)
# from uniform 1, 5
s2_1 = np.array([
1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813,
3.637
])
s2 = np.diag(np.concatenate((s2_1, np.ones(D - d))))
n1, n2 = np.random.multinomial(total_points, [0.5, 0.5])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x - y)
G = eclust.kernel_matrix(X, rho)
# can change the number of times we execute each experiment
# and initialization method as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 3] = run_clustering.spectral(k, X, G, z, run_times=5)
table[count, 4] = run_clustering.kmeans(k,
X,
z,
init="k-means++",
run_times=5)
table[count, 5] = run_clustering.gmm(k,
X,
z,
init="kmeans",
run_times=5)
count += 1
return table
def gauss_dimensions_pi(num_points=range(0, 180, 10), num_experiments=10):
"""Test unbalanced clusters."""
k = 2
D = 4
d = 2
N = 300
table = np.zeros((num_experiments * len(num_points), 6))
count = 0
for p in num_points:
for i in range(num_experiments):
# generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((1.5 * np.ones(d), np.zeros(D - d)))
s2 = np.diag(np.concatenate((.5 * np.ones(d), np.ones(D - d))))
pi1 = (N - p) / N / 2.
pi2 = (N + p) / N / 2.
n1, n2 = np.random.multinomial(N, [pi1, pi2])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x - y)
G = eclust.kernel_matrix(X, rho)
table[count, 0] = p
table[count, 1] = run_clustering.energy_hartigan(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 3] = run_clustering.spectral(k, X, G, z, run_times=5)
table[count, 4] = run_clustering.kmeans(k,
X,
z,
init="k-means++",
run_times=5)
table[count, 5] = run_clustering.gmm(k,
X,
z,
init="kmeans",
run_times=5)
count += 1
return table
def normal_or_lognormal(numpoints=range(10,100,10), num_experiments=100,
kind='normal'):
table = np.zeros((num_experiments*len(numpoints), 6))
count = 0
k = 2
for n in numpoints:
for i in range(num_experiments):
# generate data
D = 20
d = 5
m1 = np.zeros(D)
s1 = 0.5*np.eye(D)
m2 = 0.5*np.concatenate((np.ones(d), np.zeros(D-d)))
s2 = np.eye(D)
n1, n2 = np.random.multinomial(n, [0.5,0.5])
if kind == 'normal':
X, z = data.multivariate_normal([m1,m2], [s1,s2], [n1,n2])
else:
X, z = data.multivariate_lognormal([m1,m2], [s1,s2], [n1,n2])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
rho2 = lambda x, y: np.power(np.linalg.norm(x-y), 0.5)
G2 = eclust.kernel_matrix(X, rho2)
rho3 = lambda x, y: 2-2*np.exp(-np.linalg.norm(x-y)/2)
G3 = eclust.kernel_matrix(X, rho3)
table[count, 0] = n
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_hartigan(k, X, G2, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.energy_hartigan(k, X, G3, z,
init="k-means++", run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
count += 1
return table
def normal_or_lognormal(numpoints=range(10,100,10), num_experiments=100,
kind='normal'):
table = np.zeros((num_experiments*len(numpoints), 6))
count = 0
k = 2
for n in numpoints:
for i in range(num_experiments):
# generate data
D = 20
d = 5
m1 = np.zeros(D)
s1 = 0.5*np.eye(D)
m2 = 0.5*np.concatenate((np.ones(d), np.zeros(D-d)))
s2 = np.eye(D)
if kind == 'normal':
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n, n])
else:
X, z = data.multivariate_lognormal([m1, m2], [s1, s2], [n, n])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
rho2 = lambda x, y: np.power(np.linalg.norm(x-y), 0.5)
G2 = eclust.kernel_matrix(X, rho2)
rho3 = lambda x, y: 2-2*np.exp(-np.linalg.norm(x-y)/2)
G3 = eclust.kernel_matrix(X, rho3)
table[count, 0] = n
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_hartigan(k, X, G2, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.energy_hartigan(k, X, G3, z,
init="k-means++", run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
count += 1
return table
def normal_or_lognormal_difference(
numpoints=range(10, 100, 10), num_experiments=100, kind='normal'):
k = 2
table = []
for n in numpoints:
for i in range(num_experiments):
this_res = [n]
# generate data
D = 20
d = 5
m1 = np.zeros(D)
s1 = 0.5 * np.eye(D)
m2 = 0.5 * np.concatenate((np.ones(d), np.zeros(D - d)))
s2 = np.eye(D)
n1, n2 = np.random.multinomial(n, [0.5, 0.5])
if kind == 'normal':
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
else:
X, z = data.multivariate_lognormal([m1, m2], [s1, s2],
[n1, n2])
rho = lambda x, y: 2 - 2 * np.exp(-np.linalg.norm(x - y) / 2)
G = eclust.kernel_matrix(X, rho)
hart = run_clustering.energy_hartigan(k,
X,
G,
z,
init="k-means++",
run_times=5)
lloyd = run_clustering.energy_lloyd(k,
X,
G,
z,
init="k-means++",
run_times=5)
spectral = run_clustering.spectral(k, X, G, z, run_times=5)
this_res.append(hart - lloyd)
this_res.append(hart - spectral)
table.append(this_res)
table = np.array(table)
return table
def gauss_dimensions_mean(dimensions=range(2,100,20), total_points=200,
num_experiments=100, d=None):
# data distribution
k = 2
delta = 0.7
if not d:
d = dimensions[0]
table = np.zeros((num_experiments*len(dimensions), 6))
count = 0
for D in dimensions:
for i in range(num_experiments):
### generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((delta*np.ones(d), np.zeros(D-d)))
s2 = np.eye(D)
# flip Bernoulli coins to get number of points in each cluster
n1, n2 = np.random.multinomial(total_points, [0.5,0.5])
# get data, construct gram Matrix
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
##################
### cluster with different algorithms
# can change number of times we execute each experiment
# and initialization as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k, X, G, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.spectral(k, X, G, z,
run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
####################
count += 1
return table
def gauss_dimensions_cov(dimensions=range(2,100,20), total_points=200,
num_experiments=100, d=10):
"""High dimensions but with nontrivial covariance."""
k = 2
if not d:
d = dimensions[0]
table = np.zeros((num_experiments*len(dimensions), 6))
count = 0
for D in dimensions:
for l in range(num_experiments):
# generate data
m1 = np.zeros(D)
m2 = np.concatenate((np.ones(d), np.zeros(D-d)))
s1 = np.eye(D)
# from uniform 1, 5
s2_1 = np.array([1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813, 3.637])
s2 = np.diag(np.concatenate((s2_1, np.ones(D-d))))
n1, n2 = np.random.multinomial(total_points, [0.5,0.5])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
# can change the number of times we execute each experiment
# and initialization method as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k, X, G, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.spectral(k, X, G, z,
run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
count += 1
return table
def gauss_dimensions_mean(dimensions=range(2,100,20), num_points=[100, 100],
num_experiments=100, d=None):
# data distribution
k = 2
delta = 0.7
if not d:
d = dimensions[0]
n1, n2 = num_points
table = np.zeros((num_experiments*len(dimensions), 6))
count = 0
for D in dimensions:
for i in range(num_experiments):
# generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((delta*np.ones(d), np.zeros(D-d)))
s2 = np.eye(D)
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
# can change the number of times we execute each experiment
# and initialization method as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k, X, G, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.energy_spectral(k, X, G, z,
init="k-means++", run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
count += 1
return table
def gauss_dimensions_pi(num_points=range(0, 180, 10), num_experiments=10):
"""Test unbalanced clusters."""
k = 2
D = 4
d = 2
N = 250
table = np.zeros((num_experiments*len(num_points), 6))
count = 0
for p in num_points:
for i in range(num_experiments):
# generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((1.5*np.ones(d), np.zeros(D-d)))
s2 = np.diag(np.concatenate((.5*np.ones(d), np.ones(D-d))))
n1 = N-p
n2 = N+p
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x-y)
G = eclust.kernel_matrix(X, rho)
table[count, 0] = p
table[count, 1] = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k, X, G, z,
init="k-means++", run_times=5)
table[count, 3] = run_clustering.energy_spectral(k, X, G, z,
init="k-means++", run_times=5)
table[count, 4] = run_clustering.kmeans(k, X, z,
init="k-means++", run_times=5)
table[count, 5] = run_clustering.gmm(k, X, z,
init="kmeans", run_times=5)
count += 1
return table
def normal_or_lognormal_difference(numpoints=range(10,100,10),
num_experiments=100,
kind='normal'):
k = 2
table = []
for n in numpoints:
for i in range(num_experiments):
this_res = [n]
# generate data
D = 20
d = 5
m1 = np.zeros(D)
s1 = 0.5*np.eye(D)
m2 = 0.5*np.concatenate((np.ones(d), np.zeros(D-d)))
s2 = np.eye(D)
n1, n2 = np.random.multinomial(n, [0.5,0.5])
if kind == 'normal':
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1,n2])
else:
X, z = data.multivariate_lognormal([m1, m2], [s1, s2], [n1,n2])
rho = lambda x, y: 2-2*np.exp(-np.linalg.norm(x-y)/2)
G = eclust.kernel_matrix(X, rho)
hart = run_clustering.energy_hartigan(k, X, G, z,
init="k-means++", run_times=5)
lloyd = run_clustering.energy_lloyd(k, X, G, z,
init="k-means++", run_times=5)
spectral = run_clustering.spectral(k, X, G, z,
run_times=5)
this_res.append(hart-lloyd)
this_res.append(hart-spectral)
table.append(this_res)
table = np.array(table)
return table
def gauss_dimensions_mean(dimensions=range(2, 100, 20),
num_points=[100, 100],
num_experiments=100,
d=None):
# data distribution
k = 2
delta = 0.7
if not d:
d = dimensions[0]
n1, n2 = num_points
table = np.zeros((num_experiments * len(dimensions), 6))
count = 0
for D in dimensions:
for i in range(num_experiments):
# generate data
m1 = np.zeros(D)
s1 = np.eye(D)
m2 = np.concatenate((delta * np.ones(d), np.zeros(D - d)))
s2 = np.eye(D)
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
rho = lambda x, y: np.linalg.norm(x - y)
G = eclust.kernel_matrix(X, rho)
# can change the number of times we execute each experiment
# and initialization method as well
table[count, 0] = D
table[count, 1] = run_clustering.energy_hartigan(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 2] = run_clustering.energy_lloyd(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 3] = run_clustering.energy_spectral(k,
X,
G,
z,
init="k-means++",
run_times=5)
table[count, 4] = run_clustering.kmeans(k,
X,
z,
init="k-means++",
run_times=5)
table[count, 5] = run_clustering.gmm(k,
X,
z,
init="kmeans",
run_times=5)
count += 1
return table
#m2 = np.concatenate((delta*np.ones(d), np.zeros(D-d)))
#s2 = np.eye(D)
#n1, n2 = np.random.multinomial(N, [0.5,0.5])
#X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
d = 10
D = 200
N = 200
m1 = np.zeros(D)
m2 = np.concatenate((np.ones(d), np.zeros(D - d)))
s1 = np.eye(D)
s2_1 = np.array(
[1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813, 3.637])
s2 = np.diag(np.concatenate((s2_1, np.ones(D - d))))
n1, n2 = np.random.multinomial(N, [0.5, 0.5])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
numcols = 5
Y = np.zeros(shape=(N, numcols + 1))
Y[:, :numcols] = X[:, :numcols]
idx0 = np.where(z == 0)
idx1 = np.where(z == 1)
Y[idx0, numcols] = 0
Y[idx1, numcols] = 1
df = pd.DataFrame(Y,
columns=[r"$x_%i$" % i
for i in range(1, numcols + 1)] + ["class"])
g = sns.PairGrid(
df,
hue="class", #palette="hls",
#s1 = np.eye(D)
#m2 = np.concatenate((delta*np.ones(d), np.zeros(D-d)))
#s2 = np.eye(D)
#n1, n2 = np.random.multinomial(N, [0.5,0.5])
#X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
d = 10
D = 200
N = 200
m1 = np.zeros(D)
m2 = np.concatenate((np.ones(d), np.zeros(D-d)))
s1 = np.eye(D)
s2_1 = np.array([1.367, 3.175, 3.247, 4.403, 1.249, 1.969, 4.035, 4.237, 2.813, 3.637])
s2 = np.diag(np.concatenate((s2_1, np.ones(D-d))))
n1, n2 = np.random.multinomial(N, [0.5,0.5])
X, z = data.multivariate_normal([m1, m2], [s1, s2], [n1, n2])
numcols=5
Y = np.zeros(shape=(N,numcols+1))
Y[:,:numcols] = X[:,:numcols]
idx0 = np.where(z==0)
idx1 = np.where(z==1)
Y[idx0,numcols] = 0
Y[idx1,numcols] = 1
df = pd.DataFrame(Y,
columns=[r"$x_%i$"%i for i in range(1, numcols+1)]+["class"])
g = sns.PairGrid(df, hue="class", #palette="hls",
vars=[r"$x_%i$"%i for i in range(1, numcols+1)])
def scatter_fake_diag(x, y, *a, **kw):
import matplotlib.pyplot as plt
#sns.set_style("ticks", {"xtick.direction":"in", "ytick.direction": "in"})
from customize_plots import *
import sys
# syntetic data for testing
m1 = np.zeros(10)
m2 = 2 * np.ones(10)
m3 = 4 * np.ones(10)
m4 = 5 * np.ones(10)
s1 = np.eye(10)
s2 = np.eye(10)
s3 = np.eye(10)
s4 = np.eye(10)
n1 = n2 = n3 = n4 = 30
X, z = data.multivariate_normal([m1, m2, m3, m4], [s1, s2, s3, s4],
[n1, n2, n3, n4])
# max number of clusters
K = 10
# gap statistic with k-means
k_hat, df_kmeans = gap_statistics(X,
B=50,
K=K,
cluster_func=kmeans,
type_ref="svd")
print k_hat
rho = lambda x, y: np.power(np.linalg.norm(x - y), 1)
G = eclust.kernel_matrix(X, rho)
gaps = eigenvalues(G, K)
import matplotlib.pyplot as plt
#sns.set_style("ticks", {"xtick.direction":"in", "ytick.direction": "in"})
from customize_plots import *
import sys
# syntetic data for testing
m1 = np.zeros(10)
m2 = 2*np.ones(10)
m3 = 4*np.ones(10)
m4 = 5*np.ones(10)
s1 = np.eye(10)
s2 = np.eye(10)
s3 = np.eye(10)
s4 = np.eye(10)
n1 = n2 = n3 = n4 = 30
X, z = data.multivariate_normal([m1,m2,m3,m4], [s1,s2,s3,s4], [n1,n2,n3,n4])
# max number of clusters
K = 10
# gap statistic with k-means
k_hat, df_kmeans = gap_statistics(X, B=50, K=K, cluster_func=kmeans,
type_ref="svd")
print k_hat
rho = lambda x, y: np.power(np.linalg.norm(x-y), 1)
G = eclust.kernel_matrix(X, rho)
gaps = eigenvalues(G, K)
rho2 = lambda x, y: 2-2*np.exp(-np.power(np.linalg.norm(x-y),2)/2/((2)**2))
G2 = eclust.kernel_matrix(X, rho2)
