def prob_model2(bigram, lang, param):
if bigram not in bigrams_list:
value = np.exp(0)
else:
tup = (bigram, lang)
pos_l = label_list.index(lang) * len(character_bigram_vocab)
pos_k = bigrams_list.index(bigram)
pos = pos_l + pos_k
if tup in model2_features:
value = param[pos] * model2_features[tup]
else:
value = np.float64(0)
value = np.exp(value)
lower = 0
for key in character_vocab:
try:
new_bigram = bigram[0] + key
tup = (new_bigram, lang)
pos_k = bigrams_list.index(new_bigram)
pos = pos_l + pos_k
if tup in model2_features:
new_value = param[pos] * model2_features[tup]
else:
new_value = np.float64(0)
new_value = np.exp(new_value)
lower += new_value
except:
continue
if lower == 0:
prob = 1
else:
prob = value / lower
return prob
def get_step_size_function(self, x, d):
def f(l): return self.__call__(x + l * d)
_derivative = autograd.grad(f)
_derivative2 = autograd.grad(_derivative)
f.derivative = lambda l: _derivative(np.float64(l))
f.derivative2 = lambda l: _derivative2(np.float64(l))
f.domain = lambda l, gamma: self.domain(x, l, d, gamma)
return f
def dictionaries_model2():
bigrams_list = []
model2_features = {}
for key in character_bigram_vocab:
bigrams_list.append(key)
for label in label_list:
for key in character_bigram_vocab:
tup = (key, label)
tup2 = (key[0], label)
if tup in label_bigram_vocab:
value = label_bigram_vocab[tup] / (np.float64(
label_unigram_vocab[tup2]))
model2_features[tup] = value
else:
model2_features[tup] = (np.float64(0))
return (model2_features, bigrams_list)
def condition(args, to_shape=None):
"""Condition n-d args for PyCO2SYS.
If NumPy can broadcast the args together, they are a valid combination, and they
will be combined following NumPy broadcasting rules.
All array-like args will be broadcast into the same shape.
Any scalar args will be left as scalars.
"""
try: # check all args can be broadcast together
args = {k: v for k, v in args.items() if v is not None}
args_broadcast = broadcast1024(*args.values())
if to_shape is not None:
try: # check args can be broadcast to to_shape, if provided
broadcast1024(np.ones(to_shape), np.ones(args_broadcast.shape))
args_broadcast_shape = to_shape
except ValueError:
print("PyCO2SYS error: args are not broadcastable to to_shape.")
return
else:
args_broadcast_shape = args_broadcast.shape
# Broadcast the non-scalar args to a consistent shape
args_conditioned = {
k: np.broadcast_to(v, args_broadcast_shape) if not np.isscalar(v) else v
for k, v in args.items()
}
# Convert to float, where needed
args_conditioned = {
k: np.float64(v) if k in input_floats else v
for k, v in args_conditioned.items()
}
except ValueError:
print("PyCO2SYS error: input shapes cannot be broadcast together.")
return
return args_conditioned
def model1(param):
N = len(words_label_list)
V = len(words_vocab)
J = 0
for i in range(0, len(words_label_list)):
if words_label_list[i] in words_label_vocab:
upper = np.float64(words_label_vocab[words_label_list[i]])
else:
upper = np.float64(0)
if words_list[i] in words_vocab:
lower = np.float64(words_vocab[words_list[i]])
else:
lower = np.float64(0)
prob = (upper + np.exp(param)) / ((lower + (np.exp(param) * V)))
J += np.log(prob)
J = J / N
return -J
def Error_function(W_b, V_init, u_init, target, output_nrns, horizon):
W = W_b[:W_b.shape[0] - 1, :].astype(np.float64)
b = W_b[-1, :].astype(np.float64)
V = V_init.astype(np.float64)
u = u_init.astype(np.float64)
dt = np.float64(0.2)
alpha = np.float64(0.004)
beta = np.float64(0.005)
E = np.float64(0)
for i in range(horizon):
V_new = V + dt * ((fr_fun(V) @ W).flatten() + b - u)
u_new = u + dt * alpha * (beta * V - u)
V = V_new
u = u_new
s = fr_fun(V)
E += np.sum((s[output_nrns] - target[i, output_nrns])**2)
return E
def saving_dictionaries():
character_bigram_features = {}
label_bigram_features = {}
V = len(character_vocab)
for key in character_bigram_vocab:
upper = np.float64(character_bigram_vocab[key])
lower = np.float64(character_vocab[key[0]])
prob = (upper + 1) / (np.float64(lower + V))
character_bigram_features[key] = prob
for label in label_list:
for key in character_bigram_vocab:
tup = (key, label)
if tup in label_bigram_vocab:
value = label_bigram_vocab[tup] / (np.float64(
character_bigram_vocab[key]))
label_bigram_features[tup] = value
else:
label_bigram_features[tup] = (np.float64(0))
return (character_bigram_features, label_bigram_features)
def model2(param):
N = len(r_list)
J = 0
total = np.float64(sum(lang_count.values()))
for i in r_list:
lang = words_label_list[i][0]
word = words_label_list[i][1]
prob = lang_count[lang] / total
for j in range(0, len(word) - 1):
bigram = word[j] + word[j + 1]
prob_bi = prob_model2(bigram, lang, param)
prob = prob * prob_bi
if prob > 1:
print prob
J += np.log(prob)
J = J / N
return -J
def __init__(self,
model: Union[IModel, IDifferentiable],
target: np.array,
beta: np.float64 = np.float64(1)) -> None:
"""
Negative Lower Confidence Bound acquisition function for target vector
estimation. For more information see:
Efficient Bayesian Optimization for Target Vector Estimation
Uhrenholt, Anders K. and Jensen, Bjorn S.
2019, AISTATS
:param model: model that is used to compute the improvement.
:param target: target to be estimated.
:param beta: Exploration / exploitation parameter.
"""
self.model = model
self.target = target
self.k = target.shape[-1]
self.beta = beta
self.grad_fun = None
def adam(alp, b1, b2, model, init):
eps = 1.0e-8
m = 0
u = 0
t = 0
tol = 10
gradi = grad(model)
x = agnp.float64(init)
accf = model(x)
while (tol > 1.0e-3) | (accf > 1e-5):
t += 1
g = gradi(x)
m = b1 * m + (1 - b1) * g
u = b2 * u + (1 - b2) * g**2
a_t = alp * agnp.sqrt(1 - b2**t) / (1 - b1**t)
xn = x - a_t * m / (agnp.sqrt(u) + accf * eps)
tol = agnp.linalg.norm(xn - x)
x = xn
accf = model(x)
return (x, accf)
def prediction_model2(filename, label_list, param):
total = np.float64(sum(lang_count.values()))
true_labels = []
words = []
predicted_labels = []
with open(filename) as data_file:
data = json.load(data_file)
counter = 0
for item in data:
for i in range(0, len(data[item])):
lang = data[item][i]['lang']
true_labels.append(lang)
text = data[item][i]['text']
words.append(text)
counter += 1
if counter > 200:
break
for item in words:
maximum = 0
predict = ""
for label in label_list:
prob = lang_count[label] / total
for j in range(0, len(item) - 1):
bigram = item[j] + item[j + 1]
prob_bi = prob_model2(bigram, label, param)
prob = prob * prob_bi
if prob > 1:
print prob
if prob > maximum:
maximum = prob
predict = label
print predict
predicted_labels.append(predict)
return (true_labels, predicted_labels)
def apply(self, structures, mixture_coefs=None):
"""Apply constraints using given structure(s).
Compute negative log likelhood for each constraint using the given
structure.
Parameters
----------
structures : array or autograd SequenceBox or list of structures
3D chromatin structure(s) for which to compute the constraint.
Returns
-------
dict
Dictionary of constraint names and negative log likelihoods.
"""
if len(self.lambdas) == 0 or sum(self.lambdas.values()) == 0:
return {}
if mixture_coefs is None:
mixture_coefs = [1.]
if not (isinstance(structures, list)
or isinstance(structures, SequenceBox)):
structures = [structures]
if len(structures) != len(mixture_coefs):
raise ValueError(
"The number of structures (%d) and of mixture coefficents (%d)"
" should be identical." %
(len(structures), len(mixture_coefs)))
obj = {k: ag_np.float64(0.) for k, v in self.lambdas.items() if v != 0}
for struct, gamma in zip(structures, mixture_coefs):
if 'bcc' in self.lambdas and self.lambdas['bcc']:
neighbor_dis = ag_np.sqrt(
(ag_np.square(struct[self.row_adj] -
struct[self.col_adj])).sum(axis=1))
n_edges = neighbor_dis.shape[0]
obj['bcc'] = obj['bcc'] + gamma * self.lambdas['bcc'] * \
(n_edges * ag_np.square(neighbor_dis).sum() / ag_np.square(
neighbor_dis.sum()) - 1.)
if 'hsc' in self.lambdas and self.lambdas['hsc']:
nbeads_per_homo = self.lengths_lowres.sum()
struct_masked = ag_np.where(self.torm_3d, np.nan, struct)
homo1 = struct_masked[:nbeads_per_homo, :]
homo2 = struct_masked[nbeads_per_homo:, :]
begin = end = 0
for i in range(len(self.lengths_lowres)):
end = end + self.lengths_lowres[i]
homo_dis = ag_np.sqrt(
ag_np.square(
np.nanmean(homo1[begin:end, :], axis=0) -
np.nanmean(homo2[begin:end, :], axis=0)).sum())
obj['hsc'] = obj['hsc'] + gamma * self.lambdas['hsc'] * \
ag_np.square(ag_np.array([float(
self.params['hsc'][i]) - homo_dis, 0]).max())
begin = end
if 'struct' in self.lambdas and self.lambdas['struct']:
struct_myres = decrease_struct_res(
struct,
multiscale_factor=struct.shape[0] /
self.params['struct'].shape[0],
lengths_prev=self.lengths).reshape(-1, 3)
obj['struct'] = obj['struct'] + gamma * \
self.lambdas['struct'] * ag_np.sqrt(np.nanmean(ag_np.square(
self.params['struct'] - struct_myres))) / (
self.params['struct'].max() - self.params['struct'].min())
# Check constraints objective
for k, v in obj.items():
if ag_np.isnan(v):
raise ValueError("Constraint %s is nan" % k)
elif ag_np.isinf(v):
raise ValueError("Constraint %s is infinite" % k)
return {'obj_' + k: v for k, v in obj.items()}
def test_tangent_vector_multiplication(self):
# Regression test for https://github.com/pymanopt/pymanopt/issues/49.
manifold = Product((Euclidean(12), Grassmann(12, 3)))
x = manifold.random_point()
eta = manifold.random_tangent_vector(x)
np.float64(1.0) * eta
