Here are the examples of the python api autograd.numpy.array taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
55 Examples
3
View Complete Implementation : frisk.py
Copyright MIT License
Author : andymiller
Copyright MIT License
Author : andymiller
def process_dataset():
data_dir = os.path.dirname(__file__)
df = pd.read_csv(os.path.join(data_dir, 'data/frisk/frisk_with_noise.dat'), skiprows=6, delim_whitespace=True)
# compute proportion black in precinct, black = 1
# first aggregate by precinct/ethnicity, and sum over populations
popdf = df[['pop', 'precinct', 'eth']]. \
groupby(['precinct', 'eth'])['pop'].apply(sum)
percent_black = np.array([ popdf[i][1] / float(popdf[i].sum())
for i in xrange(1, 76)] )
precinct_type = pd.cut(percent_black, [0, .1, .4, 1.]) #
df['precinct_type'] = precinct_type.codes[df.precinct.values-1]
return df
3
View Complete Implementation : numpy_grads.py
Copyright MIT License
Author : BB-UCL
Copyright MIT License
Author : BB-UCL
def forward_grad_np_var(g, ans, gvs, vs, x, axis=None, ddof=0, keepdims=False):
if axis is None:
if gvs.iscomplex:
num_reps = gvs.size / 2
else:
num_reps = gvs.size
elif isinstance(axis, int):
num_reps = gvs.shape[axis]
elif isinstance(axis, tuple):
num_reps = anp.prod(anp.array(gvs.shape)[list(axis)])
x_minus_mean = anp.conj(x - anp.mean(x, axis=axis, keepdims=True))
return (2.0 * anp.sum(anp.real(g * x_minus_mean), axis=axis, keepdims=keepdims) /
(num_reps - ddof))
3
View Complete Implementation : numpy_grads.py
Copyright MIT License
Author : BB-UCL
Copyright MIT License
Author : BB-UCL
def forward_grad_np_std(g, ans, gvs, vs, x, axis=None, ddof=0, keepdims=False):
if axis is None:
if gvs.iscomplex:
num_reps = gvs.size / 2
else:
num_reps = gvs.size
elif isinstance(axis, int):
num_reps = gvs.shape[axis]
elif isinstance(axis, tuple):
num_reps = anp.prod(anp.array(gvs.shape)[list(axis)])
if num_reps <= 1:
return vs.zeros()
x_minus_mean = anp.conj(x - anp.mean(x, axis=axis, keepdims=True))
return (anp.sum(anp.real(g * x_minus_mean), axis=axis, keepdims=keepdims) /
((num_reps - ddof) * ans))
3
View Complete Implementation : base.py
Copyright MIT License
Author : cryscan
Copyright MIT License
Author : cryscan
def value(p, mu0, S0, dynmodel, policy, plant, cost, H):
policy.p = rewrap(p, policy.p)
M = mu0
S = S0
L = np.array([0])
for t in range(H):
M, S = plant.prop(M, S, plant, dynmodel, policy)
L = L + cost.gamma**t * cost.fcn(M, S)
return L
3
View Complete Implementation : ExperienceReplay.py
Copyright MIT License
Author : dtak
Copyright MIT License
Author : dtak
def rand_unif_sample(self, n):
"""Returns a random uniform sample of n experiences.
Arguments:
n -- number of transitions to sample
"""
indices = npr.choice(range(self.size), replace=False, size=n)
exp_batch = np.array(self.exp_buffer)[indices]
return np.reshape(exp_batch,(n, -1))
3
View Complete Implementation : test_likelihood.py
Copyright MIT License
Author : KeplerGO
Copyright MIT License
Author : KeplerGO
@pytest.mark.parametrize("counts, ans, opt_kwargs",
([np.array([30, 30]), 0.5, {'optimizer': 'minimize', 'x0': 0.3, 'method': 'Nelder-Mead'}],
[np.array([90, 10]), 0.9, {'optimizer': 'minimize', 'x0': 0.8, 'method': 'Nelder-Mead'}],
[np.array([30, 30]), 0.5, {'optimizer': 'differential_evolution', 'bounds': [(0, 1)], 'tol': 1e-8}],
[np.array([90, 10]), 0.9, {'optimizer': 'differential_evolution', 'bounds': [(0, 1)], 'tol': 1e-8}]))
def test_multinomial_likelihood(counts, ans, opt_kwargs):
ber_pmf = lambda p: npa.array([p, 1 - p])
logL = MultinomialLikelihood(data=counts, mean=ber_pmf)
p_hat = logL.fit(**opt_kwargs)
np.testing.astert_almost_equal(logL.uncertainties(p_hat.x),
sqrt(p_hat.x[0] * (1 - p_hat.x[0]) / counts.sum()))
astert abs(p_hat.x - ans) < 0.05
# yyyytical jeffrey's prior
neg_log_jeff_prior = 0.5 * (np.log(p_hat.x) + np.log(1 - p_hat.x) - np.log(counts.sum()))
np.testing.astert_almost_equal(neg_log_jeff_prior, logL.jeffreys_prior(p_hat.x))
np.testing.astert_almost_equal(logL.gradient(p_hat.x), 0., decimal=2)
3
View Complete Implementation : component.py
Copyright MIT License
Author : lanius
Copyright MIT License
Author : lanius
def matrix(self, _):
"""Return translation matrix in hemogeneous coordinates."""
x, y, z = self.coord
return np.array([
[1., 0., 0., x],
[0., 1., 0., y],
[0., 0., 1., z],
[0., 0., 0., 1.]
])
3
View Complete Implementation : ilqr_policy.py
Copyright MIT License
Author : Lukeeeeee
Copyright MIT License
Author : Lukeeeeee
def forward(self, obs, **kwargs):
obs = make_batch(obs, original_shape=self.env_spec.obs_shape).tolist()
action = []
if 'step' in kwargs:
step = kwargs['step']
else:
step = None
for obs_i in obs:
action_i = self._forward(obs_i, step=step)
action.append(action_i)
return np.array(action)
3
View Complete Implementation : g.py
Copyright Apache License 2.0
Author : msu-coinlab
Copyright Apache License 2.0
Author : msu-coinlab
def _calc_pareto_set(self):
return anp.array(
[3.16246061572185, 3.12833142812967, 3.09479212988791, 3.06145059523469, 3.02792915885555, 2.99382606701730,
2.95866871765285, 2.92184227312450, 0.49482511456933, 0.48835711005490, 0.48231642711865, 0.47664475092742,
0.47129550835493, 0.46623099264167, 0.46142004984199, 0.45683664767217, 0.45245876903267, 0.44826762241853,
0.44424700958760, 0.44038285956317])
3
View Complete Implementation : g.py
Copyright Apache License 2.0
Author : msu-coinlab
Copyright Apache License 2.0
Author : msu-coinlab
def __init__(self):
self.n_var = 5
self.n_constr = 6
self.n_obj = 1
self.func = self._evaluate
self.xl = anp.array([78, 33, 27, 27, 27])
self.xu = anp.array([102, 45, 45, 45, 45])
super(G4, self).__init__(n_var=self.n_var, n_obj=self.n_obj, n_constr=self.n_constr, xl=self.xl, xu=self.xu,
type_var=anp.double)