Python numpy.all() Examples
The following are 30 code examples for showing how to use numpy.all(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: libTLDA Author: wmkouw File: suba.py License: MIT License | 6 votes |
def is_pos_def(self, A): """ Check for positive definiteness. Parameters --------- A : array square symmetric matrix. Returns ------- bool whether matrix is positive-definite. Warning! Returns false for arrays containing inf or NaN. """ # Check for valid numbers if np.any(np.isnan(A)) or np.any(np.isinf(A)): return False else: return np.all(np.real(np.linalg.eigvals(A)) > 0)
Example 2
Project: libTLDA Author: wmkouw File: test_util.py License: MIT License | 6 votes |
def test_one_hot(): """Check if one_hot returns correct label matrices.""" # Generate label vector y = np.hstack((np.ones((10,))*0, np.ones((10,))*1, np.ones((10,))*2)) # Map to matrix Y, labels = one_hot(y) # Check for only 0's and 1's assert len(np.setdiff1d(np.unique(Y), [0, 1])) == 0 # Check for correct labels assert np.all(labels == np.unique(y)) # Check correct shape of matrix assert Y.shape[0] == y.shape[0] assert Y.shape[1] == len(labels)
Example 3
Project: aospy Author: spencerahill File: test_region.py License: Apache License 2.0 | 6 votes |
def test_region_init(): region = Region( name='test', description='region description', west_bound=0., east_bound=5, south_bound=0, north_bound=90., do_land_mask=True ) assert region.name == 'test' assert region.description == 'region description' assert isinstance(region.mask_bounds, tuple) assert len(region.mask_bounds) == 1 assert isinstance(region.mask_bounds[0], BoundsRect) assert np.all(region.mask_bounds[0] == (Longitude(0.), Longitude(5), 0, 90.)) assert region.do_land_mask is True
Example 4
Project: DDPAE-video-prediction Author: jthsieh File: metrics.py License: MIT License | 6 votes |
def find_match(self, pred, gt): ''' Match component to balls. ''' batch_size, n_frames_input, n_components, _ = pred.shape diff = pred.reshape(batch_size, n_frames_input, n_components, 1, 2) - \ gt.reshape(batch_size, n_frames_input, 1, n_components, 2) diff = np.sum(np.sum(diff ** 2, axis=-1), axis=1) # Direct indices indices = np.argmin(diff, axis=2) ambiguous = np.zeros(batch_size, dtype=np.int8) for i in range(batch_size): _, counts = np.unique(indices[i], return_counts=True) if not np.all(counts == 1): ambiguous[i] = 1 return indices, ambiguous
Example 5
Project: dustmaps Author: gregreen File: test_bayestar.py License: GNU General Public License v2.0 | 6 votes |
def test_bounds(self): """ Test that out-of-bounds coordinates return NaN reddening, and that in-bounds coordinates do not return NaN reddening. """ for mode in (['random_sample', 'random_sample_per_pix', 'median', 'samples', 'mean']): # Draw random coordinates, both above and below dec = -30 degree line n_pix = 1000 ra = -180. + 360.*np.random.random(n_pix) dec = -75. + 90.*np.random.random(n_pix) # 45 degrees above/below c = coords.SkyCoord(ra, dec, frame='icrs', unit='deg') ebv_calc = self._bayestar(c, mode=mode) nan_below = np.isnan(ebv_calc[dec < -35.]) nan_above = np.isnan(ebv_calc[dec > -25.]) pct_nan_above = np.sum(nan_above) / float(nan_above.size) # print r'{:s}: {:.5f}% nan above dec=-25 deg.'.format(mode, 100.*pct_nan_above) self.assertTrue(np.all(nan_below)) self.assertTrue(pct_nan_above < 0.05)
Example 6
Project: models Author: kipoi File: dataloader_m.py License: MIT License | 6 votes |
def map_values(values, pos, target_pos, dtype=None, nan=dat.CPG_NAN): """Maps `values` array at positions `pos` to `target_pos`. Inserts `nan` for uncovered positions. """ assert len(values) == len(pos) assert np.all(pos == np.sort(pos)) assert np.all(target_pos == np.sort(target_pos)) values = values.ravel() pos = pos.ravel() target_pos = target_pos.ravel() idx = np.in1d(pos, target_pos) pos = pos[idx] values = values[idx] if not dtype: dtype = values.dtype target_values = np.empty(len(target_pos), dtype=dtype) target_values.fill(nan) idx = np.in1d(target_pos, pos).nonzero()[0] assert len(idx) == len(values) assert np.all(target_pos[idx] == pos) target_values[idx] = values return target_values
Example 7
Project: neuropythy Author: noahbenson File: core.py License: GNU Affero General Public License v3.0 | 6 votes |
def image_to_cortex(self, image, surface='midgray', hemi=None, affine=Ellipsis, method=None, fill=0, dtype=None, weights=None): ''' sub.image_to_cortex(image) is equivalent to the tuple (sub.lh.from_image(image), sub.rh.from_image(image)). sub.image_to_cortex(image, surface) uses the given surface (see also cortex.surface). ''' if hemi is None: hemi = 'both' hemi = hemi.lower() if hemi in ['both', 'lr', 'all', 'auto']: return tuple( [self.image_to_cortex(image, surface=surface, hemi=h, affine=affine, method=method, fill=fill, dtype=dtype, weights=weights) for h in ['lh', 'rh']]) else: hemi = getattr(self, hemi) return hemi.from_image(image, surface=surface, affine=affine, method=method, fill=fill, dtype=dtype, weights=weights)
Example 8
Project: fullrmc Author: bachiraoun File: StructureFactorConstraints.py License: GNU Affero General Public License v3.0 | 6 votes |
def get_constraint_value(self, applyMultiframePrior=True): """ Compute all partial Structure Factor (SQs). :Parameters: #. applyMultiframePrior (boolean): Whether to apply subframe weight and prior to the total. This will only have an effect when used frame is a subframe and in case subframe weight and prior is defined. :Returns: #. SQs (dictionary): The SQs dictionnary, where keys are the element wise intra and inter molecular SQs and values are the computed SQs. """ if self.data is None: LOGGER.warn("data must be computed first using 'compute_data' method.") return {} return self._get_constraint_value(self.data, applyMultiframePrior=applyMultiframePrior)
Example 9
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: coco.py License: Apache License 2.0 | 6 votes |
def getImgIds(self, imgIds=[], catIds=[]): ''' Get img ids that satisfy given filter conditions. :param imgIds (int array) : get imgs for given ids :param catIds (int array) : get imgs with all given cats :return: ids (int array) : integer array of img ids ''' imgIds = imgIds if type(imgIds) == list else [imgIds] catIds = catIds if type(catIds) == list else [catIds] if len(imgIds) == len(catIds) == 0: ids = self.imgs.keys() else: ids = set(imgIds) for i, catId in enumerate(catIds): if i == 0 and len(ids) == 0: ids = set(self.catToImgs[catId]) else: ids &= set(self.catToImgs[catId]) return list(ids)
Example 10
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: coco.py License: Apache License 2.0 | 6 votes |
def annToRLE(self, ann): """ Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array) """ t = self.imgs[ann['image_id']] h, w = t['height'], t['width'] segm = ann['segmentation'] if type(segm) == list: # polygon -- a single object might consist of multiple parts # we merge all parts into one mask rle code # rles = maskUtils.frPyObjects(segm, h, w) # rle = maskUtils.merge(rles) raise NotImplementedError("maskUtils disabled!") elif type(segm['counts']) == list: # uncompressed RLE # rle = maskUtils.frPyObjects(segm, h, w) raise NotImplementedError("maskUtils disabled!") else: # rle rle = ann['segmentation'] return rle
Example 11
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: test_module.py License: Apache License 2.0 | 6 votes |
def test_module_input_grads(): a = mx.sym.Variable('a', __layout__='NC') b = mx.sym.Variable('b', __layout__='NC') c = mx.sym.Variable('c', __layout__='NC') c = a + 2 * b + 3 * c net = mx.mod.Module(c, data_names=['b', 'c', 'a'], label_names=None, context=[mx.cpu(0), mx.cpu(1)]) net.bind(data_shapes=[['b', (5, 5)], ['c', (5, 5)], ['a', (5, 5)]], label_shapes=None, inputs_need_grad=True) net.init_params() net.forward(data_batch=mx.io.DataBatch(data=[nd.ones((5, 5)), nd.ones((5, 5)), nd.ones((5, 5))])) net.backward(out_grads=[nd.ones((5, 5))]) input_grads = net.get_input_grads() b_grad = input_grads[0].asnumpy() c_grad = input_grads[1].asnumpy() a_grad = input_grads[2].asnumpy() assert np.all(a_grad == 1), a_grad assert np.all(b_grad == 2), b_grad assert np.all(c_grad == 3), c_grad
Example 12
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: test_module.py License: Apache License 2.0 | 6 votes |
def test_module_reshape(): data = mx.sym.Variable('data') sym = mx.sym.FullyConnected(data, num_hidden=20, name='fc') dshape = (7, 20) mod = mx.mod.Module(sym, ('data',), None, context=[mx.cpu(0), mx.cpu(1)]) mod.bind(data_shapes=[('data', dshape)]) mod.init_params() mod.init_optimizer(optimizer_params={'learning_rate': 1}) mod.forward(mx.io.DataBatch(data=[mx.nd.ones(dshape)], label=None)) mod.backward([mx.nd.ones(dshape)]) mod.update() assert mod.get_outputs()[0].shape == dshape assert (mod.get_params()[0]['fc_bias'].asnumpy() == -1).all() dshape = (14, 20) mod.reshape(data_shapes=[('data', dshape)]) mod.forward(mx.io.DataBatch(data=[mx.nd.ones(dshape)], label=None)) mod.backward([mx.nd.ones(dshape)]) mod.update() assert mod.get_outputs()[0].shape == dshape assert (mod.get_params()[0]['fc_bias'].asnumpy() == -3).all()
Example 13
Project: dynamic-training-with-apache-mxnet-on-aws Author: awslabs File: test_sparse_ndarray.py License: Apache License 2.0 | 6 votes |
def test_sparse_nd_setitem(): def check_sparse_nd_setitem(stype, shape, dst): x = mx.nd.zeros(shape=shape, stype=stype) x[:] = dst dst_nd = mx.nd.array(dst) if isinstance(dst, (np.ndarray, np.generic)) else dst assert np.all(x.asnumpy() == dst_nd.asnumpy() if isinstance(dst_nd, NDArray) else dst) shape = rand_shape_2d() for stype in ['row_sparse', 'csr']: # ndarray assignment check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, 'default')) check_sparse_nd_setitem(stype, shape, rand_ndarray(shape, stype)) # numpy assignment check_sparse_nd_setitem(stype, shape, np.ones(shape)) # scalar assigned to row_sparse NDArray check_sparse_nd_setitem('row_sparse', shape, 2)
Example 14
Project: DOTA_models Author: ringringyi File: map_utils.py License: Apache License 2.0 | 6 votes |
def _project_to_map(map, vertex, wt=None, ignore_points_outside_map=False): """Projects points to map, returns how many points are present at each location.""" num_points = np.zeros((map.size[1], map.size[0])) vertex_ = vertex[:, :2] - map.origin vertex_ = np.round(vertex_ / map.resolution).astype(np.int) if ignore_points_outside_map: good_ind = np.all(np.array([vertex_[:,1] >= 0, vertex_[:,1] < map.size[1], vertex_[:,0] >= 0, vertex_[:,0] < map.size[0]]), axis=0) vertex_ = vertex_[good_ind, :] if wt is not None: wt = wt[good_ind, :] if wt is None: np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), 1) else: assert(wt.shape[0] == vertex.shape[0]), \ 'number of weights should be same as vertices.' np.add.at(num_points, (vertex_[:, 1], vertex_[:, 0]), wt) return num_points
Example 15
Project: DOTA_models Author: ringringyi File: nav_env.py License: Apache License 2.0 | 6 votes |
def raw_valid_fn_vec(self, xyt): """Returns if the given set of nodes is valid or not.""" height = self.traversible.shape[0] width = self.traversible.shape[1] x = np.round(xyt[:,[0]]).astype(np.int32) y = np.round(xyt[:,[1]]).astype(np.int32) is_inside = np.all(np.concatenate((x >= 0, y >= 0, x < width, y < height), axis=1), axis=1) x = np.minimum(np.maximum(x, 0), width-1) y = np.minimum(np.maximum(y, 0), height-1) ind = np.ravel_multi_index((y,x), self.traversible.shape) is_traversible = self.traversible.ravel()[ind] is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible), axis=1), axis=1) return is_valid
Example 16
Project: DOTA_models Author: ringringyi File: nav_env.py License: Apache License 2.0 | 6 votes |
def valid_fn_vec(self, pqr): """Returns if the given set of nodes is valid or not.""" xyt = self.to_actual_xyt_vec(np.array(pqr)) height = self.traversible.shape[0] width = self.traversible.shape[1] x = np.round(xyt[:,[0]]).astype(np.int32) y = np.round(xyt[:,[1]]).astype(np.int32) is_inside = np.all(np.concatenate((x >= 0, y >= 0, x < width, y < height), axis=1), axis=1) x = np.minimum(np.maximum(x, 0), width-1) y = np.minimum(np.maximum(y, 0), height-1) ind = np.ravel_multi_index((y,x), self.traversible.shape) is_traversible = self.traversible.ravel()[ind] is_valid = np.all(np.concatenate((is_inside[:,np.newaxis], is_traversible), axis=1), axis=1) return is_valid
Example 17
Project: libTLDA Author: wmkouw File: util.py License: MIT License | 5 votes |
def is_pos_def(X): """Check for positive definiteness.""" return np.all(np.linalg.eigvals(X) > 0)
Example 18
Project: libTLDA Author: wmkouw File: tcpr.py License: MIT License | 5 votes |
def remove_intercept(self, X): """Remove 1's from data as last features.""" # Data shape N, D = X.shape # Find which column contains the intercept intercept_index = [] for d in range(D): if np.all(X[:, d] == 0): intercept_index.append(d) # Remove intercept columns X = X[:, np.setdiff1d(np.arange(D), intercept_index)] return X, D-len(intercept_index)
Example 19
Project: libTLDA Author: wmkouw File: suba.py License: MIT License | 5 votes |
def is_pos_def(self, A): """Check for positive definiteness.""" return np.all(np.real(np.linalg.eigvals(A)) > 0)
Example 20
Project: libTLDA Author: wmkouw File: suba.py License: MIT License | 5 votes |
def find_medioid(self, X, Y): """ Find point with minimal distance to all other points. Parameters ---------- X : array data set, with N samples x D features. Y : array labels to select for which samples to compute distances. Returns ------- x : array medioid ix : int index of medioid """ # Initiate an array with infinities A = np.full((X.shape[0],), np.inf) # Insert sum of distances to other points A[Y] = np.sum(squareform(pdist(X[Y, :])), axis=1) # Find the index of the point with the smallest distance ix = np.argmin(A) return X[ix, :], ix
Example 21
Project: libTLDA Author: wmkouw File: test_iw.py License: MIT License | 5 votes |
def test_iwe_ratio_Gaussians(): """Test for estimating ratio of Gaussians.""" X = rnd.randn(10, 2) Z = rnd.randn(10, 2) + 1 clf = ImportanceWeightedClassifier() iw = clf.iwe_ratio_gaussians(X, Z) assert np.all(iw >= 0)
Example 22
Project: libTLDA Author: wmkouw File: test_iw.py License: MIT License | 5 votes |
def test_iwe_logistic_discrimination(): """Test for estimating through logistic classifier.""" X = rnd.randn(10, 2) Z = rnd.randn(10, 2) + 1 clf = ImportanceWeightedClassifier() iw = clf.iwe_logistic_discrimination(X, Z) assert np.all(iw >= 0)
Example 23
Project: libTLDA Author: wmkouw File: test_iw.py License: MIT License | 5 votes |
def test_iwe_kernel_densities(): """Test for estimating through kernel density estimation.""" X = rnd.randn(10, 2) Z = rnd.randn(10, 2) + 1 clf = ImportanceWeightedClassifier() iw = clf.iwe_kernel_densities(X, Z) assert np.all(iw >= 0)
Example 24
Project: libTLDA Author: wmkouw File: test_iw.py License: MIT License | 5 votes |
def test_iwe_kernel_mean_matching(): """Test for estimating through kernel mean matching.""" X = rnd.randn(10, 2) Z = rnd.randn(10, 2) + 1 clf = ImportanceWeightedClassifier() iw = clf.iwe_kernel_mean_matching(X, Z) assert np.all(iw >= 0)
Example 25
Project: aospy Author: spencerahill File: vertcoord.py License: Apache License 2.0 | 5 votes |
def does_coord_increase_w_index(arr): """Determine if the array values increase with the index. Useful, e.g., for pressure, which sometimes is indexed surface to TOA and sometimes the opposite. """ diff = np.diff(arr) if not np.all(np.abs(np.sign(diff))): raise ValueError("Array is not monotonic: {}".format(arr)) # Since we know its monotonic, just test the first value. return bool(diff[0])
Example 26
Project: aospy Author: spencerahill File: test_region.py License: Apache License 2.0 | 5 votes |
def test_region_init_mult_rect(): bounds_in = [[1, 2, 3, 4], (-12, -30, 2.3, 9)] region = Region(name='test', mask_bounds=bounds_in) assert isinstance(region.mask_bounds, tuple) assert len(region.mask_bounds) == 2 for (w, e, s, n), bounds in zip(bounds_in, region.mask_bounds): assert isinstance(bounds, tuple) assert np.all(bounds == (Longitude(w), Longitude(e), s, n))
Example 27
Project: aospy Author: spencerahill File: test_utils_longitude.py License: Apache License 2.0 | 5 votes |
def test_lon_eq(obj1, obj2): assert np.all(obj1 == obj2) assert np.all(obj2 == obj1)
Example 28
Project: aospy Author: spencerahill File: test_utils_longitude.py License: Apache License 2.0 | 5 votes |
def test_lon_lt(obj1, obj2): assert np.all(obj1 < obj2) assert np.all(obj2 > obj1)
Example 29
Project: aospy Author: spencerahill File: test_utils_longitude.py License: Apache License 2.0 | 5 votes |
def test_lon_leq(obj1, obj2): assert np.all(obj1 <= obj2) assert np.all(obj2 >= obj2)
Example 30
Project: aospy Author: spencerahill File: test_utils_longitude.py License: Apache License 2.0 | 5 votes |
def test_lon_geq(obj1, obj2): assert np.all(obj1 >= obj2) assert np.all(obj2 <= obj1)