Here are the examples of the python api numpy. taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
3 Examples
0
Example 1
def __init__(self, length):
# Use an array of Python 'long' ints which conveniently grow
# as large as necessary. It's about 50X slower though...
self.store = np.array([0L] * length, dtype=object)
0
Example 2
Project: diogenes Source File: test_utils.py
def test_convert_to_sa(self):
# already a structured array
sa = np.array([(1, 1.0, 'a', datetime(2015, 01, 01)),
(2, 2.0, 'b', datetime(2016, 01, 01))],
dtype=[('int', int), ('float', float), ('str', 'O'),
('date', 'M8[s]')])
self.assertTrue(np.array_equal(sa, utils.convert_to_sa(sa)))
# humogeneous array no col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('f0', int), ('f1', int), ('f2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(nd)))
# humogeneous array with col names provided
nd = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
ctrl = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
dtype=[('i0', int), ('i1', int), ('i2', int)])
self.assertTrue(np.array_equal(ctrl, utils.convert_to_sa(
nd,
col_names=['i0', 'i1', 'i2'])))
# list of lists no col name provided
lol = [[1, 1, None],
['abc', 2, 3.4]]
ctrl = np.array([('1', 1, np.nan),
('abc', 2, 3.4)],
dtype=[('f0', 'S3'), ('f1', int), ('f2', float)])
res = utils.convert_to_sa(lol)
self.assertTrue(uft.array_equal(ctrl, res))
# list of lists with col name provided
lol = [['hello', 1.2, datetime(2012, 1, 1), None],
[1.3, np.nan, None, '2013-01-01'],
[1.4, 1.5, '2014-01-01', 'NO_SUCH_RECORD']]
ctrl = np.array([('hello', 1.2, datetime(2012, 1, 1), utils.NOT_A_TIME),
('1.3', np.nan, utils.NOT_A_TIME, datetime(2013, 1, 1)),
('1.4', 1.5, datetime(2014, 1, 1), utils.NOT_A_TIME)],
dtype=[('i0', 'S5'), ('i1', float), ('i2', 'M8[us]'),
('i3', 'M8[us]')])
res = utils.convert_to_sa(lol, col_names = ['i0', 'i1', 'i2', 'i3'])
self.assertTrue(uft.array_equal(ctrl, res))
0
Example 3
Project: python-rl Source File: batch_model.py
def predictSet(self, states):
pred = []
known = self.areKnown(states)
states = self.normStates(states)
for a in range(self.numActions):
if self.has_fit[a]:
predictions = numpy.array(map(lambda (m,p): m.predict(states[a]) if p is None else numpy.ones((len(states[a]),))*p,
zip(self.model[a], self.predConst[a]))).T
pState = predictions[:,2:]
pTerminate = self.model_termination(predictions[:,1], known[a])
pRewards = self.exploration_reward(states[a], known[a], predictions[:,0])
ranges = self.getStateSpace()[0]
if self.params['relative']:
pred += [(self.denormState((pState + states[a]).clip(min=0, max=1)), pRewards, pTerminate.clip(min=0, max=1))]
else:
pred += [(self.denormState(pState.clip(min=0, max=1)), pRewards, pTerminate.clip(min=0, max=1))]
else:
pred += [([None]*len(states[a]), [None]*len(states[a]), [None]*len(states[a]))]
return pred