我们从Python开源项目中，提取了以下28个代码示例，用于说明如何使用numpy.exp2()。
def __init__(self, bounds, orig_resolution, tile_width, tile_height, tile_format_url, zoom_level=0, missing_z=None, image_leaf_shape=None): self.orig_bounds = bounds self.orig_resolution = orig_resolution self.tile_width = tile_width self.tile_height = tile_height self.tile_format_url = tile_format_url self.zoom_level = int(zoom_level) if missing_z is None: missing_z = [] self.missing_z = frozenset(missing_z) if image_leaf_shape is None: image_leaf_shape = [10, tile_height, tile_width] scale = np.exp2(np.array([0, self.zoom_level, self.zoom_level])).astype(np.int64) data_shape = (np.zeros(3), np.divide(bounds, scale).astype(np.int64)) self.image_data = OctreeVolume(image_leaf_shape, data_shape, 'float32', populator=self.image_populator) self.label_data = None
def perplexity(self): log_likelihood = 0. for doc_ix in xrange(self.num_docs): prob_topic_given_document = np.zeros(self.K) for i in xrange(self.K): prob_topic_given_document[i] = (self.ndt[doc_ix][i] + self.alpha) / ( self.nd[doc_ix] + self.K * self.alpha) for word in self.corpus[doc_ix]: prob_word_given_topic = np.zeros(self.K) word_ix = self.word2ix[word] for j in xrange(self.K): prob_word_given_topic[j] = (self.ntw[j][word_ix] + self.beta) / (self.nt[j] + self.K * self.alpha) prob_word = 0. for j in xrange(self.K): prob_word += prob_topic_given_document[j] * prob_word_given_topic[j] log_likelihood += np.log(prob_word) perplexity = np.exp2(-log_likelihood / self.num_docs) return perplexity
def zero_fill(Ix, zff): """ Zero-fill interferogram. Assymetric to prevent zpd from changing index. Args: Ix (np.array): 1D array with a single interferogram zff (int): Zero-filling factor Returns: Ix_zff: 1D array of Ix + zero fill """ N = Ix.shape[0] # Calculate next power of two for DFT efficiency N_2 = int(np.exp2(np.ceil(np.log2(N)))) # fill to N**2 * zff zero_fill = ((N_2 - N) + (N_2 * (zff))) Ix_zff = np.hstack((Ix, np.zeros(zero_fill))) return Ix_zff
def test_log1p_compiler_shenanigans(self): # Check if log1p is behaving on 32 bit intel systems. assert_(np.isfinite(np.log1p(np.exp2(-53))))
def test_exp2_values(self): x = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024] y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10] for dt in ['f', 'd', 'g']: xf = np.array(x, dtype=dt) yf = np.array(y, dtype=dt) assert_almost_equal(np.exp2(yf), xf)
def undo_log_transform_if_needed(data_df, prov_code): """Undo log transformation if L2X is in prov_code.""" if LOG_TRANSFORM_PROV_CODE_ENTRY in prov_code: out_df = np.exp2(data_df) else: out_df = data_df return out_df
def exp2(x): return x.__class__(numpy.exp2(x))
def __init__(self, parent, downsample): self.scale = np.exp2(downsample).astype(np.int64) super(DownsampledVolume, self).__init__( parent, np.multiply(parent.resolution, self.scale), image_data=parent.image_data, label_data=parent.label_data, mask_data=parent.mask_data)
def resolution(self): return self.orig_resolution * np.exp2([0, self.zoom_level, self.zoom_level])
def __init__(self, leaf_shape, bounds, dtype, populator=None): self.leaf_shape = np.asarray(leaf_shape).astype(np.int64) self.bounds = (np.asarray(bounds[0], dtype=np.int64), np.asarray(bounds[1], dtype=np.int64)) self.dtype = np.dtype(dtype) self.populator = populator ceil_bounds = self.leaf_shape * \ np.exp2(np.ceil(np.log2((self.bounds[1] - self.bounds[0]) / self.leaf_shape.astype(np.float64)))).astype(np.int64).max() self.root_node = BranchNode(self, (self.bounds[0], self.bounds[0] + ceil_bounds), clip_bound=self.bounds[1])
def log_perplexity(self, chunk): """Return per-word lower bound on log perplexity. Also logs this and perplexity at INFO level. """ vw_data = self._predict(chunk)[1] corpus_words = sum(cnt for document in chunk for _, cnt in document) bound = -vw_data['average_loss'] LOG.info("%.3f per-word bound, %.1f perplexity estimate based on a " "held-out corpus of %i documents with %i words", bound, numpy.exp2(-bound), vw_data['corpus_size'], corpus_words) return bound
def test_numpy_method(): # This type of code is used frequently by PyMC3 users x = tt.dmatrix('x') data = np.random.rand(5, 5) x.tag.test_value = data for fct in [np.arccos, np.arccosh, np.arcsin, np.arcsinh, np.arctan, np.arctanh, np.ceil, np.cos, np.cosh, np.deg2rad, np.exp, np.exp2, np.expm1, np.floor, np.log, np.log10, np.log1p, np.log2, np.rad2deg, np.sin, np.sinh, np.sqrt, np.tan, np.tanh, np.trunc]: y = fct(x) f = theano.function([x], y) utt.assert_allclose(np.nan_to_num(f(data)), np.nan_to_num(fct(data)))
def impl(self, x): # If x is an int8 or uint8, numpy.exp2 will compute the result in # half-precision (float16), where we want float32. x_dtype = str(getattr(x, 'dtype', '')) if x_dtype in ('int8', 'uint8'): return numpy.exp2(x, sig='f') return numpy.exp2(x)
def grad(self, inputs, gout): (x,) = inputs (gz,) = gout if x.type in complex_types: raise NotImplementedError() if self(x).type in discrete_types: if x.type in discrete_types: return [x.zeros_like(dtype=theano.config.floatX)] else: return [x.zeros_like()] return gz * exp2(x) * log(numpy.cast[x.type](2)),
def c_code(self, node, name, inputs, outputs, sub): (x,) = inputs (z,) = outputs if node.inputs[0].type in complex_types: raise NotImplementedError('type not supported', type) return "%(z)s = exp2(%(x)s);" % locals()
def test_numpy_ufuncs(self): # test ufuncs of numpy 1.9.2. see: # http://docs.scipy.org/doc/numpy/reference/ufuncs.html # some functions are skipped because it may return different result # for unicode input depending on numpy version for name, idx in compat.iteritems(self.indices): for func in [np.exp, np.exp2, np.expm1, np.log, np.log2, np.log10, np.log1p, np.sqrt, np.sin, np.cos, np.tan, np.arcsin, np.arccos, np.arctan, np.sinh, np.cosh, np.tanh, np.arcsinh, np.arccosh, np.arctanh, np.deg2rad, np.rad2deg]: if isinstance(idx, pd.tseries.base.DatetimeIndexOpsMixin): # raise TypeError or ValueError (PeriodIndex) # PeriodIndex behavior should be changed in future version with tm.assertRaises(Exception): func(idx) elif isinstance(idx, (Float64Index, Int64Index)): # coerces to float (e.g. np.sin) result = func(idx) exp = Index(func(idx.values), name=idx.name) self.assert_index_equal(result, exp) self.assertIsInstance(result, pd.Float64Index) else: # raise AttributeError or TypeError if len(idx) == 0: continue else: with tm.assertRaises(Exception): func(idx) for func in [np.isfinite, np.isinf, np.isnan, np.signbit]: if isinstance(idx, pd.tseries.base.DatetimeIndexOpsMixin): # raise TypeError or ValueError (PeriodIndex) with tm.assertRaises(Exception): func(idx) elif isinstance(idx, (Float64Index, Int64Index)): # results in bool array result = func(idx) exp = func(idx.values) self.assertIsInstance(result, np.ndarray) tm.assertNotIsInstance(result, Index) else: if len(idx) == 0: continue else: with tm.assertRaises(Exception): func(idx)
