def main():
np.random.seed(12)
data_dim = 8
n_data = 10
threshold_missing = 0.5
mu = np.random.randn(data_dim, 1)
sigma = make_spd_matrix(
n_dim=data_dim) # Generate a random positive semi-definite matrix
# test if the matrix is positive definite
# print(is_pos_def(sigma))
x_full = gauss.gauss_sample(mu, sigma, n_data)
missing = np.random.rand(n_data, data_dim) < threshold_missing
x_miss = np.copy(x_full)
x_miss[missing] = np.nan
x_imputed = gauss.gauss_impute(mu, sigma, x_miss)
#Create a matrix from x_miss by replacing the NaNs with 0s to display the hinton_diagram
xmiss0 = np.copy(x_miss)
for g in np.argwhere(np.isnan(x_miss)):
xmiss0[g[0], g[1]] = 0
plot_1 = plt.figure(1)
pml.hinton_diagram(xmiss0, ax=plot_1.gca())
plot_1.suptitle('Observed')
pml.savefig("gauss_impute_observed.pdf", dpi=300)
plot_2 = plt.figure(2)
pml.hinton_diagram(x_full, ax=plot_2.gca())
plot_2.suptitle('Hidden truth')
pml.savefig("gauss_impute_truth.pdf", dpi=300)
plot_3 = plt.figure(3)
pml.hinton_diagram(x_imputed, ax=plot_3.gca())
plot_3.suptitle('Imputation with true params')
pml.savefig("gauss_impute_pred.pdf", dpi=300)
plt.show()
from sklearn.datasets import make_spd_matrix
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
np.random.seed(4)
data_dim = 4
n_data = 100
threshold_missing = 0.5
mu = np.random.randn(data_dim, 1)
sigma = make_spd_matrix(n_dim=data_dim)
x_full = gauss.gauss_sample(mu, sigma, n_data)
missing = np.random.rand(n_data, data_dim) < threshold_missing
x_miss = np.copy(x_full)
x_miss[missing] = np.nan
x_impute_oracle = gauss.gauss_impute(mu, sigma, x_miss)
result = gauss.gauss_fit_em(x_miss)
m = result.get('mu')
sig = result.get('Sigma')
x_impute_em = gauss.gauss_impute(m, sig, x_miss)
def plot_performance(x_miss, x_full, x_impute):
r_squared = []
for i in range(4):
miss = np.argwhere(np.isnan(x_miss[:, i]))
r2 = r2_score(x_full[miss, i], x_impute[miss, i])
r_squared.append(r2)
fig, axs = plt.subplots(nrows=2, ncols=2)
import pyprobml_utils as pml
np.random.seed(12)
data_dim = 8
n_data = 10
threshold_missing = 0.5
mu = np.random.randn(data_dim, 1)
sigma = make_spd_matrix(
n_dim=data_dim) # Generate a random positive semi-definite matrix
# test if the matrix is positive definite
# print(is_pos_def(sigma))
x_full = gauss.gauss_sample(mu, sigma, n_data)
missing = np.random.rand(n_data, data_dim) < threshold_missing
x_miss = np.copy(x_full)
x_miss[missing] = np.nan
x_imputed = gauss.gauss_impute(mu, sigma, x_miss)
#Create a matrix from x_miss by replacing the NaNs with 0s to display the hinton_diagram
xmiss0 = np.copy(x_miss)
for g in np.argwhere(np.isnan(x_miss)):
xmiss0[g[0], g[1]] = 0
plot_1 = plt.figure(1)
pml.hinton_diagram(xmiss0, ax=plot_1.gca())
plot_1.suptitle('Observed')
pml.savefig("gauss_impute_observed.pdf", dpi=300)
plot_2 = plt.figure(2)
pml.hinton_diagram(x_full, ax=plot_2.gca())
plot_2.suptitle('Hidden truth')
pml.savefig("gauss_impute_truth.pdf", dpi=300)