Python numpy.append() Examples
The following are 30
code examples of numpy.append().
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.
You may also want to check out all available functions/classes of the module
numpy
, or try the search function
.
Example #1
Source File: test_operator_gpu.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 6 votes |
def test_embedding_with_type(): def test_embedding_helper(data_types, weight_types, low_pad, high_pad): NVD = [[20, 10, 20], [200, 10, 300]] for N, V, D in NVD: sym = mx.sym.Embedding(name='embedding', input_dim=V, output_dim=D) ctx_list = [] for data_type in data_types: for weight_type in weight_types: ctx_list.append({'ctx': mx.gpu(0), 'embedding_data': (N,), 'type_dict': {'embedding_data': data_type, 'embedding_weight': weight_type}}) ctx_list.append({'ctx': mx.cpu(0), 'embedding_data': (N,), 'type_dict': {'embedding_data': data_type, 'embedding_weight': weight_type}}) arg_params = {'embedding_data': np.random.randint(low=-low_pad, high=V+high_pad, size=(N,))} check_consistency(sym, ctx_list, grad_req={'embedding_data': 'null','embedding_weight': 'write'}, arg_params=arg_params) data_types = [np.float16, np.float32, np.float64, np.int32] weight_types = [np.float16, np.float32, np.float64] test_embedding_helper(data_types, weight_types, 5, 5) data_types = [np.uint8] weight_types = [np.float16, np.float32, np.float64] test_embedding_helper(data_types, weight_types, 0, 5)
Example #2
Source File: base.py From pymesh with MIT License | 6 votes |
def join(self, another): """ :param m: BaseMesh :return: """ if another is None: raise AttributeError("another BaseMesh instance is required") if not isinstance(another, BaseMesh): raise TypeError("anther must be an instance of BaseMesh") self.data = numpy.append(self.data, another.data) self.normals = numpy.append(self.normals, another.normals, axis=0) self.vectors = numpy.append(self.vectors, another.vectors, axis=0) self.attr = numpy.append(self.attr, another.attr, axis=0) return self
Example #3
Source File: gtf_utils.py From models with MIT License | 6 votes |
def add_exon(self, chrom, strand, start, stop): if strand != self.strand or chrom != self.chrom: print("The exon has different chrom or strand to the transcript.") return _exon = np.array([start, stop], "int").reshape(1, 2) self.exons = np.append(self.exons, _exon, axis=0) self.exons = np.sort(self.exons, axis=0) self.tranL += abs(int(stop) - int(start) + 1) self.exonNum += 1 self.seglen = np.zeros(self.exons.shape[0] * 2 - 1, "int") self.seglen[0] = self.exons[0, 1] - self.exons[0, 0] + 1 for i in range(1, self.exons.shape[0]): self.seglen[i * 2 - 1] = self.exons[i, 0] - self.exons[i - 1, 1] - 1 self.seglen[i * 2] = self.exons[i, 1] - self.exons[i, 0] + 1 if ["-", "-1", "0", 0, -1].count(self.strand) > 0: self.seglen = self.seglen[::-1]
Example #4
Source File: ModelingCloth.py From Modeling-Cloth with MIT License | 6 votes |
def zxy_grid(co_y, tymin, tymax, subs, c, t, c_peat, t_peat): # create linespace grid between bottom and top of tri z #subs = 7 t_min = np.min(tymin) t_max = np.max(tymax) divs = np.linspace(t_min, t_max, num=subs, dtype=np.float32) # figure out which triangles and which co are in each section co_bools = (co_y > divs[:-1][:, nax]) & (co_y < divs[1:][:, nax]) tri_bools = (tymin < divs[1:][:, nax]) & (tymax > divs[:-1][:, nax]) for i, j in zip(co_bools, tri_bools): if (np.sum(i) > 0) & (np.sum(j) > 0): c3 = c[i] t3 = t[j] c_peat.append(np.repeat(c3, t3.shape[0])) t_peat.append(np.tile(t3, c3.shape[0]))
Example #5
Source File: dataloader.py From models with MIT License | 6 votes |
def add_exon(self, chrom, strand, start, stop): if strand != self.strand or chrom != self.chrom: print("The exon has different chrom or strand to the transcript.") return _exon = np.array([start, stop], "int").reshape(1,2) self.exons = np.append(self.exons, _exon, axis=0) self.exons = np.sort(self.exons, axis=0) self.tranL += abs(int(stop) - int(start) + 1) self.exonNum += 1 self.seglen = np.zeros(self.exons.shape[0] * 2 - 1, "int") self.seglen[0] = self.exons[0,1]-self.exons[0,0] + 1 for i in range(1, self.exons.shape[0]): self.seglen[i*2-1] = self.exons[i,0]-self.exons[i-1,1] - 1 self.seglen[i*2] = self.exons[i,1]-self.exons[i,0] + 1 if ["-","-1","0",0,-1].count(self.strand) > 0: self.seglen = self.seglen[::-1]
Example #6
Source File: gtf_utils.py From models with MIT License | 6 votes |
def add_exon(self, chrom, strand, start, stop): if strand != self.strand or chrom != self.chrom: print("The exon has different chrom or strand to the transcript.") return _exon = np.array([start, stop], "int").reshape(1, 2) self.exons = np.append(self.exons, _exon, axis=0) self.exons = np.sort(self.exons, axis=0) self.tranL += abs(int(stop) - int(start) + 1) self.exonNum += 1 self.seglen = np.zeros(self.exons.shape[0] * 2 - 1, "int") self.seglen[0] = self.exons[0, 1] - self.exons[0, 0] + 1 for i in range(1, self.exons.shape[0]): self.seglen[i * 2 - 1] = self.exons[i, 0] - self.exons[i - 1, 1] - 1 self.seglen[i * 2] = self.exons[i, 1] - self.exons[i, 0] + 1 if ["-", "-1", "0", 0, -1].count(self.strand) > 0: self.seglen = self.seglen[::-1]
Example #7
Source File: TargetList.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License | 6 votes |
def loadAliasFile(self): """ Args: Returns: alias (): list """ #OLD aliasname = 'alias_4_11_2019.pkl' aliasname = 'alias_10_07_2019.pkl' tmp1 = inspect.getfile(self.__class__).split('/')[:-2] tmp1.append('util') self.classpath = '/'.join(tmp1) #self.classpath = os.path.split(inspect.getfile(self.__class__))[0] #vprint(inspect.getfile(self.__class__)) self.alias_datapath = os.path.join(self.classpath, aliasname) #Load pkl and outspec files try: with open(self.alias_datapath, 'rb') as f:#load from cache alias = pickle.load(f, encoding='latin1') except: vprint('Failed to open fullPathPKL %s'%self.alias_datapath) pass return alias ##########################################################
Example #8
Source File: dataloader.py From models with MIT License | 6 votes |
def add_exon(self, chrom, strand, start, stop): if strand != self.strand or chrom != self.chrom: print("The exon has different chrom or strand to the transcript.") return _exon = np.array([start, stop], "int").reshape(1,2) self.exons = np.append(self.exons, _exon, axis=0) self.exons = np.sort(self.exons, axis=0) self.tranL += abs(int(stop) - int(start) + 1) self.exonNum += 1 self.seglen = np.zeros(self.exons.shape[0] * 2 - 1, "int") self.seglen[0] = self.exons[0,1]-self.exons[0,0] + 1 for i in range(1, self.exons.shape[0]): self.seglen[i*2-1] = self.exons[i,0]-self.exons[i-1,1] - 1 self.seglen[i*2] = self.exons[i,1]-self.exons[i,0] + 1 if ["-","-1","0",0,-1].count(self.strand) > 0: self.seglen = self.seglen[::-1]
Example #9
Source File: TargetList.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License | 6 votes |
def stellar_mass(self): """Populates target list with 'true' and 'approximate' stellar masses This method calculates stellar mass via the formula relating absolute V magnitude and stellar mass. The values are in units of solar mass. Function called by reset sim """ # 'approximate' stellar mass self.MsEst = (10.**(0.002456*self.MV**2 - 0.09711*self.MV + 0.4365))*u.solMass # normally distributed 'error' err = (np.random.random(len(self.MV))*2. - 1.)*0.07 self.MsTrue = (1. + err)*self.MsEst # if additional filters are desired, need self.catalog_atts fully populated if not hasattr(self.catalog_atts,'MsEst'): self.catalog_atts.append('MsEst') if not hasattr(self.catalog_atts,'MsTrue'): self.catalog_atts.append('MsTrue')
Example #10
Source File: gtf_utils.py From models with MIT License | 6 votes |
def add_exon(self, chrom, strand, start, stop): if strand != self.strand or chrom != self.chrom: print("The exon has different chrom or strand to the transcript.") return _exon = np.array([start, stop], "int").reshape(1, 2) self.exons = np.append(self.exons, _exon, axis=0) self.exons = np.sort(self.exons, axis=0) self.tranL += abs(int(stop) - int(start) + 1) self.exonNum += 1 self.seglen = np.zeros(self.exons.shape[0] * 2 - 1, "int") self.seglen[0] = self.exons[0, 1] - self.exons[0, 0] + 1 for i in range(1, self.exons.shape[0]): self.seglen[i * 2 - 1] = self.exons[i, 0] - self.exons[i - 1, 1] - 1 self.seglen[i * 2] = self.exons[i, 1] - self.exons[i, 0] + 1 if ["-", "-1", "0", 0, -1].count(self.strand) > 0: self.seglen = self.seglen[::-1]
Example #11
Source File: create_encodings.py From Face-Recognition with MIT License | 6 votes |
def create_dataset(training_dir_path, labels): X = [] for i in _zipped_folders_labels_images(training_dir_path, labels): for fileName in i[2]: file_path = os.path.join(i[0], fileName) img = face_recognition_api.load_image_file(file_path) imgEncoding = face_recognition_api.face_encodings(img) if len(imgEncoding) > 1: print('\x1b[0;37;43m' + 'More than one face found in {}. Only considering the first face.'.format(file_path) + '\x1b[0m') if len(imgEncoding) == 0: print('\x1b[0;37;41m' + 'No face found in {}. Ignoring file.'.format(file_path) + '\x1b[0m') else: print('Encoded {} successfully.'.format(file_path)) X.append(np.append(imgEncoding[0], i[1])) return X
Example #12
Source File: buffers_of_buffers.py From PyOptiX with MIT License | 6 votes |
def create_random_buffer(max_width, max_height): scale = randf() w = int(max(max_width * scale, 1)) h = int(max(max_height * scale, 1)) arr = [] red, green, blue = randf(), randf(), randf() for y in range(h): arr.append([]) for x in range(w): if randf() < 0.1: arr[y].append([red * 255.0, green * 255.0, blue * 255.0, 255]) else: arr[y].append([255, 255, 255, 0]) return Buffer.from_array(np.array(arr, dtype=np.uint8), buffer_type='i', drop_last_dim=True)
Example #13
Source File: test_operator_gpu.py From dynamic-training-with-apache-mxnet-on-aws with Apache License 2.0 | 6 votes |
def test_elementwisesum_with_type(): dev_types = [[mx.gpu(0), [np.float64, np.float32, np.float16]], [mx.cpu(0), [np.float64, np.float32]] ] for num_args in range(1, 6): ews_arg_shape = {} for i in range(num_args): ews_arg_shape['ews_arg'+str(i)] = (2, 10) sym = mx.sym.ElementWiseSum(name='ews', num_args=num_args) ctx_list = [] for dev, types in dev_types: for dtype in types: ews_arg_dtype = {'type_dict':{}} for i in range(num_args): ews_arg_dtype['type_dict']['ews_arg'+str(i)] = dtype ctx_elem = {'ctx': dev} ctx_elem.update(ews_arg_shape) ctx_elem.update(ews_arg_dtype) ctx_list.append(ctx_elem) check_consistency(sym, ctx_list)
Example #14
Source File: filters.py From HardRLWithYoutube with MIT License | 5 votes |
def __call__(self, x, update=True): self.stack.append(x) while len(self.stack) < self.stack.maxlen: self.stack.append(x) return np.concatenate(self.stack, axis=-1)
Example #15
Source File: filters.py From HardRLWithYoutube with MIT License | 5 votes |
def __call__(self, x, update=True): return np.append(x, self.count/100.0)
Example #16
Source File: TargetList.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License | 5 votes |
def setOfStarsWithKnownPlanets(self, data): """ From the data dict created in this script, this method extracts the set of unique star names Args: data (dict): dict containing the pl_hostname of each star Returns: list (list): list of star names with a known planet """ starNames = list() for i in np.arange(len(data)): starNames.append(data[i]['pl_hostname']) return list(set(starNames))
Example #17
Source File: trpo_mpi.py From HardRLWithYoutube with MIT License | 5 votes |
def add_vtarg_and_adv(seg, gamma, lam): new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 vpred = np.append(seg["vpred"], seg["nextvpred"]) T = len(seg["rew"]) seg["adv"] = gaelam = np.empty(T, 'float32') rew = seg["rew"] lastgaelam = 0 for t in reversed(range(T)): nonterminal = 1-new[t+1] delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam seg["tdlamret"] = seg["adv"] + seg["vpred"]
Example #18
Source File: trpo_mpi.py From HardRLWithYoutube with MIT License | 5 votes |
def add_vtarg_and_adv(seg, gamma, lam): new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 vpred = np.append(seg["vpred"], seg["nextvpred"]) T = len(seg["rew"]) seg["adv"] = gaelam = np.empty(T, 'float32') rew = seg["rew"] lastgaelam = 0 for t in reversed(range(T)): nonterminal = 1-new[t+1] delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam seg["tdlamret"] = seg["adv"] + seg["vpred"]
Example #19
Source File: TargetList.py From EXOSIMS with BSD 3-Clause "New" or "Revised" License | 5 votes |
def fgk_filter(self): """Includes only F, G, K spectral type stars in Target List """ spec = np.array(list(map(str, self.Spec))) iF = np.where(np.core.defchararray.startswith(spec, 'F'))[0] iG = np.where(np.core.defchararray.startswith(spec, 'G'))[0] iK = np.where(np.core.defchararray.startswith(spec, 'K'))[0] i = np.append(np.append(iF, iG), iK) i = np.unique(i) self.revise_lists(i)
Example #20
Source File: pposgd_simple.py From lirpg with MIT License | 5 votes |
def add_vtarg_and_adv(seg, gamma, lam): """ Compute target value using TD(lambda) estimator, and advantage with GAE(lambda) """ new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 vpred = np.append(seg["vpred"], seg["nextvpred"]) T = len(seg["rew"]) seg["adv"] = gaelam = np.empty(T, 'float32') rew = seg["rew"] lastgaelam = 0 for t in reversed(range(T)): nonterminal = 1-new[t+1] delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam seg["tdlamret"] = seg["adv"] + seg["vpred"]
Example #21
Source File: trpo_mpi.py From lirpg with MIT License | 5 votes |
def add_vtarg_and_adv(seg, gamma, lam): new = np.append(seg["new"], 0) # last element is only used for last vtarg, but we already zeroed it if last new = 1 vpred = np.append(seg["vpred"], seg["nextvpred"]) T = len(seg["rew"]) seg["adv"] = gaelam = np.empty(T, 'float32') rew = seg["rew"] lastgaelam = 0 for t in reversed(range(T)): nonterminal = 1-new[t+1] delta = rew[t] + gamma * vpred[t+1] * nonterminal - vpred[t] gaelam[t] = lastgaelam = delta + gamma * lam * nonterminal * lastgaelam seg["tdlamret"] = seg["adv"] + seg["vpred"]
Example #22
Source File: nn.py From Kaggler with MIT License | 5 votes |
def predict_raw(self, X): """Predict targets for a feature matrix. Args: X (np.array of float): feature matrix for prediction """ # b -- bias for the input and h layers b = np.ones((X.shape[0], 1)) w2 = self.w[-(self.h + 1):].reshape(self.h + 1, 1) w1 = self.w[:-(self.h + 1)].reshape(self.i + 1, self.h) # Make X to have the same number of columns as self.i. # Because of the sparse matrix representation, X for prediction can # have a different number of columns. if X.shape[1] > self.i: # If X has more columns, cut extra columns. X = X[:, :self.i] elif X.shape[1] < self.i: # If X has less columns, cut the rows of the weight matrix between # the input and h layers instead of X itself because the SciPy # sparse matrix does not support .set_shape() yet. idx = range(X.shape[1]) idx.append(self.i) # Include the last row for the bias w1 = w1[idx, :] if sparse.issparse(X): return np.hstack((sigm(sparse.hstack((X, b)).dot(w1)), b)).dot(w2) else: return np.hstack((sigm(np.hstack((X, b)).dot(w1)), b)).dot(w2)
Example #23
Source File: function_helper.py From TradzQAI with Apache License 2.0 | 5 votes |
def fill_for_noncomputable_vals(input_data, result_data): non_computable_values = np.repeat( np.nan, len(input_data) - len(result_data) ) filled_result_data = np.append(non_computable_values, result_data) return filled_result_data
Example #24
Source File: create_encodings.py From Face-Recognition with MIT License | 5 votes |
def _filter_image_files(training_dir_path): exts = [".jpg", ".jpeg", ".png"] training_folder_files_list = [] for list_files in _get_each_labels_files(training_dir_path): l = [] for file in list_files: imageName, ext = os.path.splitext(file) if ext.lower() in exts: l.append(file) training_folder_files_list.append(l) return training_folder_files_list
Example #25
Source File: ssresnet.py From deep-models with Apache License 2.0 | 5 votes |
def load_data(files, data_dir, label_count): data, labels = load_data_one(data_dir + '/' + files[0]) for f in files[1:]: data_n, labels_n = load_data_one(data_dir + '/' + f) data = np.append(data, data_n, axis=0) labels = np.append(labels, labels_n, axis=0) labels = np.array([ [ float(i == label) for i in xrange(label_count) ] for label in labels ]) return data, labels
Example #26
Source File: bcfstore.py From Caffe-Python-Data-Layer with BSD 2-Clause "Simplified" License | 5 votes |
def __init__(self, filename): self._filename = filename print 'Loading BCF file to memory ... '+filename file = open(filename, 'rb') size = numpy.fromstring(file.read(8), dtype=numpy.uint64) file_sizes = numpy.fromstring(file.read(8*size), dtype=numpy.uint64) self._offsets = numpy.append(numpy.uint64(0), numpy.add.accumulate(file_sizes)) self._memory = file.read() file.close()
Example #27
Source File: resnet.py From deep-models with Apache License 2.0 | 5 votes |
def load_data(files, data_dir, label_count): data, labels = load_data_one(data_dir + '/' + files[0]) for f in files[1:]: data_n, labels_n = load_data_one(data_dir + '/' + f) data = np.append(data, data_n, axis=0) labels = np.append(labels, labels_n, axis=0) labels = np.array([ [ float(i == label) for i in xrange(label_count) ] for label in labels ]) return data, labels
Example #28
Source File: ModelingCloth.py From Modeling-Cloth with MIT License | 5 votes |
def get_spring_mix(ob, eidx): rs = [] ls = [] minrl = [] for i in eidx: r = eidx[eidx == i[1]].shape[0] l = eidx[eidx == i[0]].shape[0] rs.append (min(r,l)) ls.append (min(r,l)) mix = 1 / np.array(rs + ls) ** 1.2 return mix
Example #29
Source File: ModelingCloth.py From Modeling-Cloth with MIT License | 5 votes |
def collision_object_update(self, context): """Updates the collider object""" collide = self.modeling_cloth_object_collision # remove objects from dict if deleted cull_list = [] if 'colliders' in extra_data: if extra_data['colliders'] is not None: if not collide: if self.name in extra_data['colliders']: del(extra_data['colliders'][self.name]) for i in extra_data['colliders']: remove = True if i in bpy.data.objects: if bpy.data.objects[i].type == "MESH": if bpy.data.objects[i].modeling_cloth_object_collision: remove = False if remove: cull_list.append(i) for i in cull_list: del(extra_data['colliders'][i]) # add class to dict if true. if collide: if 'colliders' not in extra_data: extra_data['colliders'] = {} if extra_data['colliders'] is None: extra_data['colliders'] = {} extra_data['colliders'][self.name] = create_collider() # cloth object detect updater:
Example #30
Source File: filters.py From lirpg with MIT License | 5 votes |
def __call__(self, x, update=True): return np.append(x, self.count/100.0)