def grad_stability(X):
(dim, _) = np.shape(X)
G = np.zeros(np.shape(X))
A_1 = get_entries(X, A_1_cds)
diff1 = 2 * np.dot(A_1, mu)
grad1 = np.tile(mu.T, (block_dim, 1))
# (block_dim,1) * (block_dim,block_dim) across rows
grad1 = diff1 * grad1
set_entries(G, A_1_cds, grad1)
A_T_1 = get_entries(X, A_T_1_cds)
diffT1 = 2 * np.dot(A_T_1.T, mu)
gradT1 = np.tile(mu.T, (block_dim, 1))
gradT1 = diffT1 * gradT1
set_entries(G, A_T_1_cds, gradT1.T)
A_2 = get_entries(X, A_2_cds)
diff2 = 2 * np.dot(A_2, mu)
grad2 = np.tile(mu.T, (block_dim, 1))
grad2 = diff2 * grad2
set_entries(G, A_2_cds, grad2)
A_T_2 = get_entries(X, A_T_2_cds)
diffT2 = 2 * np.dot(A_T_2.T, mu)
gradT2 = np.tile(mu.T, (block_dim, 1))
gradT2 = diffT2 * gradT2
set_entries(G, A_T_2_cds, gradT2.T)
return (1./dim**2) * G
def grad_batch_linear_equals(X, c, P_coords, Q, R_coords):
grad = np.zeros(np.shape(X))
grad += grad_l2_batch_equals(X, c*get_entries(X, P_coords) + Q,
R_coords)
if c != 0:
grad += grad_l2_batch_equals(X,(1./c)*get_entries(X, R_coords) - Q,
P_coords)
return grad
def grad_many_batch_linear_equals(X, constraints):
grad = np.zeros(np.shape(X))
(dim, _) = np.shape(X)
for c, P_coords, Q, R_coords in constraints:
grad += grad_l2_batch_equals(X, c*get_entries(X, P_coords) + Q,
R_coords)
if c != 0:
grad += grad_l2_batch_equals(X,
(1./c)*(get_entries(X, R_coords) - Q), P_coords)
return (1./dim**2) * grad
def stability(X):
(dim, _) = np.shape(X)
A_1 = get_entries(X, A_1_cds)
diff1 = np.dot(A_1, mu)
A_T_1 = get_entries(X, A_T_1_cds)
diffT1 = np.dot(A_T_1.T, mu)
A_2 = get_entries(X, A_2_cds)
diff2 = np.dot(A_2, mu)
A_T_2 = get_entries(X, A_T_2_cds)
diffT2 = np.dot(A_T_2.T, mu)
return (1./dim**2) * (np.dot(diff1.T, diff1)
+ np.dot(diffT1.T, diffT1)
+ np.dot(diff2.T, diff2)
+ np.dot(diffT2.T, diffT2))
def get_entries(self, start_index):
json = utils.download_json(self.search_terms, start_index)
return utils.get_entries(json)
#!/usr/bin/env python3
from etym_patterns import check_etymology
from utils import extract_etymology, get_entries
success = 0
failure = 0
errors = []
for etymology in sorted((extract_etymology(entry) for entry in get_entries()),
key=lambda x: (len(x), x)):
if etymology:
c = check_etymology(etymology)
if c:
success += 1
else:
failure += 1
errors.append(etymology)
print(f"{success} successes; {failure} failures")
if errors:
for error in errors[:10]:
print(error)
#!/usr/bin/env python3
from entry_patterns import check_entry
from utils import get_entries
success = 0
failure = 0
errors = []
for entry in sorted(get_entries(), key=lambda x: (len(x), x)):
if entry:
c = check_entry(entry)
if c:
success += 1
else:
failure += 1
errors.append(entry)
print(f"{success} successes; {failure} failures")
if errors:
print()
for error in errors[:10]:
print(error)
print()
def batch_linear_equals(X, c, P_coords, Q, R_coords):
"""
Performs operation R_coords = c * P_coords + Q
"""
val = l2_batch_equals(X, c*get_entries(X, P_coords) + Q, R_coords)
return val
def grad_l2_batch_equals(X, A, coord):
# Upper right
grad_piece = 2*(get_entries(X,coord) - A)
grad = np.zeros(np.shape(X))
set_entries(grad, coord, grad_piece)
return grad
def l2_batch_equals(X, A, coord):
c = np.sum((get_entries(X,coord) - A)**2)
return c
def l1_batch_equals(X, A, coord):
c = np.sum(np.abs(get_entries(X,coord) - A))
return c
