from gp import GP, ExponentialSquaredKernel
import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
from utils import multiple_formatter
# Set values to model parameters.
lengthscale = 1
signal_variance = 1.
noise_variance = 0.1
# Create the GP.
kernel = ExponentialSquaredKernel(
lengthscale=lengthscale, signal_variance=signal_variance)
gp = GP(kernel=kernel, noise_variance=noise_variance)
n = 60
x = np.linspace(0, 2 * np.pi, n)
mean = np.zeros(n)
cov = gp.k(x, x)
# Draw samples from the GP prior.
probabilities = []
samples = []
jitter = np.eye(n) * 1e-6
for _ in range(50):
y = multivariate_normal.rvs(mean=mean, cov=cov)
# Add a jitter to the covariance matrix for numerical stability.
prob = multivariate_normal.pdf(y, mean=mean, cov=cov + jitter)
samples.append(y)
probabilities.append(prob)
from gp import GP, ExponentialSquaredKernel
import numpy as np
from scipy.stats import multivariate_normal
import matplotlib.pyplot as plt
from kernels import ConstantKernel, LinearKernel, SumKernel, ProductKernel, Matern12, Matern52, \
PeriodicKernel
from utils import multiple_formatter
# Set values to model parameters.
lengthscale = 1
signal_variance = 1.
noise_variance = 0.01
# Create the GP.
e_kernel = ExponentialSquaredKernel(lengthscale=lengthscale * 2,
signal_variance=signal_variance)
c_kernel = ConstantKernel(variance=1)
l_kernel = LinearKernel(variance=1)
p_kernel = PeriodicKernel(lengthscale=lengthscale,
signal_variance=signal_variance,
period=np.pi * 0.25)
e2_kernel = ExponentialSquaredKernel(lengthscale=lengthscale * 1000,
signal_variance=signal_variance)
m5_kernel = Matern52(lengthscale=lengthscale * 10,
signal_variance=signal_variance * 10)
m1_kernel = Matern12(lengthscale=lengthscale, signal_variance=signal_variance)