def test_regression_2(self):
np.random.seed(42)
data = np.random.randn(100) * 2.0
me = GaussianQuadraticTest(self.grad_log_normal)
U_stat,_ = me.get_statistic_multiple(data)
pval = me.compute_pvalue(U_stat)
assert pval == 0.0
def test_regression_2(self):
np.random.seed(42)
data = np.random.randn(100) * 2.0
me = GaussianQuadraticTest(self.grad_log_normal)
U_stat, _ = me.get_statistic_multiple(data)
pval = me.compute_pvalue(U_stat)
assert pval == 0.0
def test_get_statistic_multiple_equals_get_statistic(self):
N = 10
X = np.random.randn(N)
me = GaussianQuadraticTest(self.grad_log_normal)
U_matrix_multiple, stat_multiple = me.get_statistic_multiple(X)
U_matrix, stat = me.get_statisitc(N, X)
assert_allclose(stat, stat_multiple)
assert_allclose(U_matrix_multiple, U_matrix)
def test_get_statistic_multiple_equals_get_statistic(self):
N = 10
X = np.random.randn(N)
me = GaussianQuadraticTest(self.grad_log_normal)
U_matrix_multiple, stat_multiple = me.get_statistic_multiple(X)
U_matrix, stat = me.get_statisitc(N, X)
assert_allclose(stat, stat_multiple)
assert_allclose(U_matrix_multiple, U_matrix)
dfs = range(1, 4, 2)
mc_reps = 100
res = np.empty((0, 2))
block = N / np.log(N)
p_change = 1.0 / block
print(p_change)
for df in dfs:
for mc in range(mc_reps):
print(mc)
X = almost_t_student(10 * N, df, 0.01)
X = X[::10]
me = GaussianQuadraticTest(grad_log_normal)
U_stat, _ = me.get_statistic_multiple(X)
pval = me.compute_pvalues_for_processes(U_stat, p_change)
res = np.vstack((res, np.array([df, pval])))
for mc in range(mc_reps):
X = almost_t_student(10 * N, 100, 0.01)
X = X[::10]
me = GaussianQuadraticTest(grad_log_normal)
U_stat, _ = me.get_statistic_multiple(X)
pval = me.compute_pvalues_for_processes(U_stat, p_change)
res = np.vstack((res, np.array([np.Inf, pval])))
np.save('results.npy', res)
df = DataFrame(res)
grad = lambda x: est.grad(np.array([x]))[0]
s = GaussianQuadraticTest(grad)
num_bootstrap = 200
result_fname = os.path.splitext(os.path.basename(__file__))[0] + ".txt"
num_repetitions = 150
for _ in range(num_repetitions):
for m in ms_fit:
est = KernelExpFiniteGaussian(sigma, lmbda, m, D)
X_test = np.random.randn(N_test, D)
X = np.random.randn(N_fit, D)
est.fit(X)
U_matrix, stat = s.get_statistic_multiple(X_test[:,0])
bootsraped_stats = np.empty(num_bootstrap)
for i in range(num_bootstrap):
W = np.sign(np.random.randn(N_test))
WW = np.outer(W, W)
st = np.mean(U_matrix * WW)
bootsraped_stats[i] = N_test * st
p_value = np.mean(bootsraped_stats>stat)
print m, p_value
store_results(result_fname,
D=D,
N_fit=N_fit,
N_test=N_test,
grad = lambda x: est.grad(np.array([x]))[0]
s = GaussianQuadraticTest(grad)
num_bootstrap = 200
result_fname = os.path.splitext(os.path.basename(__file__))[0] + ".txt"
num_repetitions = 150
for _ in range(num_repetitions):
for m in ms_fit:
est = KernelExpFiniteGaussian(sigma, lmbda, m, D)
X_test = np.random.randn(N_test, D)
X = np.random.randn(N_fit, D)
est.fit(X)
U_matrix, stat = s.get_statistic_multiple(X_test[:, 0])
bootsraped_stats = np.empty(num_bootstrap)
for i in range(num_bootstrap):
W = np.sign(np.random.randn(N_test))
WW = np.outer(W, W)
st = np.mean(U_matrix * WW)
bootsraped_stats[i] = N_test * st
p_value = np.mean(bootsraped_stats > stat)
print m, p_value
store_results(result_fname,
D=D,
N_fit=N_fit,
N_test=N_test,
dfs = range(1, 4, 2)
mc_reps = 100
res = np.empty((0,2))
block = N/np.log(N)
p_change = 1.0/block
print(p_change)
for df in dfs:
for mc in range(mc_reps):
print(mc)
X = almost_t_student(10*N,df,0.01)
X = X[::10]
me = GaussianQuadraticTest(grad_log_normal)
U_stat,_ = me.get_statistic_multiple(X)
pval = me.compute_pvalues_for_processes(U_stat,p_change)
res = np.vstack((res,np.array([df, pval])))
for mc in range(mc_reps):
X = almost_t_student(10*N,100,0.01)
X = X[::10]
me = GaussianQuadraticTest(grad_log_normal)
U_stat,_ = me.get_statistic_multiple(X)
pval = me.compute_pvalues_for_processes(U_stat,p_change)
res = np.vstack((res,np.array([np.Inf, pval])))
np.save('results.npy',res)
df = DataFrame(res)