Python numpy.arange() Examples
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code examples of numpy.arange().
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Example #1
Source File: tcpr.py From libTLDA with MIT License | 7 votes |
def add_intercept(self, X): """Add 1's to data as last features.""" # Data shape N, D = X.shape # Check if there's not already an intercept column if np.any(np.sum(X, axis=0) == N): # Report print('Intercept is not the last feature. Swapping..') # Find which column contains the intercept intercept_index = np.argwhere(np.sum(X, axis=0) == N) # Swap intercept to last X = X[:, np.setdiff1d(np.arange(D), intercept_index)] # Add intercept as last column X = np.hstack((X, np.ones((N, 1)))) # Append column of 1's to data, and increment dimensionality return X, D+1
Example #2
Source File: input_helpers.py From deep-siamese-text-similarity with MIT License | 6 votes |
def batch_iter(self, data, batch_size, num_epochs, shuffle=True): """ Generates a batch iterator for a dataset. """ data = np.asarray(data) print(data) print(data.shape) data_size = len(data) num_batches_per_epoch = int(len(data)/batch_size) + 1 for epoch in range(num_epochs): # Shuffle the data at each epoch if shuffle: shuffle_indices = np.random.permutation(np.arange(data_size)) shuffled_data = data[shuffle_indices] else: shuffled_data = data for batch_num in range(num_batches_per_epoch): start_index = batch_num * batch_size end_index = min((batch_num + 1) * batch_size, data_size) yield shuffled_data[start_index:end_index]
Example #3
Source File: snippets.py From Collaborative-Learning-for-Weakly-Supervised-Object-Detection with MIT License | 6 votes |
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)): """ A wrapper function to generate anchors given different scales Also return the number of anchors in variable 'length' """ anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales)) A = anchors.shape[0] shift_x = np.arange(0, width) * feat_stride shift_y = np.arange(0, height) * feat_stride shift_x, shift_y = np.meshgrid(shift_x, shift_y) shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose() K = shifts.shape[0] # width changes faster, so here it is H, W, C anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2)) anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False) length = np.int32(anchors.shape[0]) return anchors, length
Example #4
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 6 votes |
def create_cifar100(tfrecord_dir, cifar100_dir): print('Loading CIFAR-100 from "%s"' % cifar100_dir) import pickle with open(os.path.join(cifar100_dir, 'train'), 'rb') as file: data = pickle.load(file, encoding='latin1') images = data['data'].reshape(-1, 3, 32, 32) labels = np.array(data['fine_labels']) assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8 assert labels.shape == (50000,) and labels.dtype == np.int32 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 99 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #5
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_generate_np_targeted_gives_adversarial_example(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), np.random.randint(0, 1, 100)] = 1 x_adv = self.attack.generate_np(x_val, max_iterations=100, binary_search_steps=3, initial_const=1, clip_min=-5, clip_max=5, batch_size=100, y_target=feed_labs) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(np.argmax(feed_labs, axis=1) == new_labs) > 0.9)
Example #6
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_generate_gives_adversarial_example(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) orig_labs = np.argmax(self.sess.run(self.model(x_val)), axis=1) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), orig_labs] = 1 x = tf.placeholder(tf.float32, x_val.shape) y = tf.placeholder(tf.float32, feed_labs.shape) x_adv_p = self.attack.generate(x, max_iterations=100, binary_search_steps=3, initial_const=1, clip_min=-5, clip_max=5, batch_size=100, y=y) self.assertEqual(x_val.shape, x_adv_p.shape) x_adv = self.sess.run(x_adv_p, {x: x_val, y: feed_labs}) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(orig_labs == new_labs) < 0.1)
Example #7
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_generate_np_targeted_gives_adversarial_example(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), np.random.randint(0, 1, 100)] = 1 x_adv = self.attack.generate_np(x_val, max_iterations=100, binary_search_steps=3, initial_const=1, clip_min=-5, clip_max=5, batch_size=100, y_target=feed_labs) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(np.argmax(feed_labs, axis=1) == new_labs) > 0.9)
Example #8
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_generate_gives_adversarial_example(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) orig_labs = np.argmax(self.sess.run(self.model(x_val)), axis=1) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), orig_labs] = 1 x = tf.placeholder(tf.float32, x_val.shape) y = tf.placeholder(tf.float32, feed_labs.shape) x_adv_p = self.attack.generate(x, max_iterations=100, binary_search_steps=3, initial_const=1, clip_min=-5, clip_max=5, batch_size=100, y=y) self.assertEqual(x_val.shape, x_adv_p.shape) x_adv = self.sess.run(x_adv_p, {x: x_val, y: feed_labs}) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(orig_labs == new_labs) < 0.1)
Example #9
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 6 votes |
def create_mnist(tfrecord_dir, mnist_dir): print('Loading MNIST from "%s"' % mnist_dir) import gzip with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file: images = np.frombuffer(file.read(), np.uint8, offset=16) with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file: labels = np.frombuffer(file.read(), np.uint8, offset=8) images = images.reshape(-1, 1, 28, 28) images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0) assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8 assert labels.shape == (60000,) and labels.dtype == np.uint8 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 9 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #10
Source File: stock.py From osqf2015 with MIT License | 6 votes |
def create(clz): """One-time creation of app's objects. This function is called once, and is responsible for creating all objects (plots, datasources, etc) """ self = clz() n_vals = 1000 self.source = ColumnDataSource( data=dict( top=[], bottom=0, left=[], right=[], x= np.arange(n_vals), values= np.random.randn(n_vals) )) # Generate a figure container self.stock_plot = clz.create_stock(self.source) self.update_data() self.children.append(self.stock_plot)
Example #11
Source File: utils.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def to_categorical(y, num_classes=None): """ Converts a class vector (integers) to binary class matrix. This is adapted from the Keras function with the same name. :param y: class vector to be converted into a matrix (integers from 0 to num_classes). :param num_classes: num_classes: total number of classes. :return: A binary matrix representation of the input. """ y = np.array(y, dtype='int').ravel() if not num_classes: num_classes = np.max(y) + 1 warnings.warn("FutureWarning: the default value of the second" "argument in function \"to_categorical\" is deprecated." "On 2018-9-19, the second argument" "will become mandatory.") n = y.shape[0] categorical = np.zeros((n, num_classes)) categorical[np.arange(n), y] = 1 return categorical
Example #12
Source File: cgp.py From cgp-cnn with MIT License | 6 votes |
def active_net_list(self): net_list = [["input", 0, 0]] active_cnt = np.arange(self.net_info.input_num + self.net_info.node_num + self.net_info.out_num) active_cnt[self.net_info.input_num:] = np.cumsum(self.is_active) for n, is_a in enumerate(self.is_active): if is_a: t = self.gene[n][0] if n < self.net_info.node_num: # intermediate node type_str = self.net_info.func_type[t] else: # output node type_str = self.net_info.out_type[t] connections = [active_cnt[self.gene[n][i+1]] for i in range(self.net_info.max_in_num)] net_list.append([type_str] + connections) return net_list # CGP with (1 + \lambda)-ES
Example #13
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 6 votes |
def test_generate_targeted_gives_adversarial_example(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), np.random.randint(0, 1, 100)] = 1 x = tf.placeholder(tf.float32, x_val.shape) y = tf.placeholder(tf.float32, feed_labs.shape) x_adv_p = self.attack.generate(x, max_iterations=100, binary_search_steps=3, initial_const=1, clip_min=-5, clip_max=5, batch_size=100, y_target=y) self.assertEqual(x_val.shape, x_adv_p.shape) x_adv = self.sess.run(x_adv_p, {x: x_val, y: feed_labs}) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(np.argmax(feed_labs, axis=1) == new_labs) > 0.9)
Example #14
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 5 votes |
def create_svhn(tfrecord_dir, svhn_dir): print('Loading SVHN from "%s"' % svhn_dir) import pickle images = [] labels = [] for batch in range(1, 4): with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file: data = pickle.load(file, encoding='latin1') images.append(data[0]) labels.append(data[1]) images = np.concatenate(images) labels = np.concatenate(labels) assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8 assert labels.shape == (73257,) and labels.dtype == np.uint8 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 9 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #15
Source File: gather.py From models with MIT License | 5 votes |
def average_labranchor(df, model_name, col_types): import numpy as np # choose the maximum diff diff_cols = df.columns.values[df.columns.astype(str).str.contains("DIFF")] model_outputs = [int(el.split("_")[-1]) for el in diff_cols] model_outputs_order = np.argsort(model_outputs) # select the model output tha gives the maximum absolute difference max_col_id = df[diff_cols[model_outputs_order]].abs().values.argmax(axis=1) # # just to be sure it will work: assert np.all(df[diff_cols[model_outputs_order]].abs().values[np.arange(len(max_col_id)), max_col_id] == df[diff_cols].abs().max(axis=1).values) # averaged = {} usable_columns = df.columns.tolist() for ct in col_types: col_sel = [col for col in usable_columns if ct in col] usable_columns = [col for col in usable_columns if col not in col_sel] if len(col_sel) == 0: continue # average model_outputs = [int(el.split("_")[-1]) for el in col_sel] model_outputs_order = np.argsort(model_outputs) # use the column selection from before keep_vals = df[np.array(col_sel)[model_outputs_order]].values[np.arange(len(max_col_id)), max_col_id] averaged[model_name + ct.lower()] = keep_vals # return pd.DataFrame(averaged, index=df.index)
Example #16
Source File: bio_utils.py From models with MIT License | 5 votes |
def one_hot(ints): dictionary_k = 4 # maximum number of nucleotides ints_len = len(ints) ints_enc = np.zeros((ints_len, dictionary_k)) ints_enc[np.arange(ints_len), [k - 1 for k in ints]] = 1 return ints_enc
Example #17
Source File: metrics.py From tensorflow-DeepFM with MIT License | 5 votes |
def gini(actual, pred): assert (len(actual) == len(pred)) all = np.asarray(np.c_[actual, pred, np.arange(len(actual))], dtype=np.float) all = all[np.lexsort((all[:, 2], -1 * all[:, 1]))] totalLosses = all[:, 0].sum() giniSum = all[:, 0].cumsum().sum() / totalLosses giniSum -= (len(actual) + 1) / 2. return giniSum / len(actual)
Example #18
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_generate_np_gives_clipped_adversarial_examples(self): x_val = np.random.rand(100, 2) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((100, 2)) feed_labs[np.arange(100), np.random.randint(0, 1, 100)] = 1 x_adv = self.attack.generate_np(x_val, max_iterations=10, binary_search_steps=1, initial_const=1, clip_min=-0.2, clip_max=0.3, batch_size=100, y_target=feed_labs) self.assertTrue(-0.201 < np.min(x_adv)) self.assertTrue(np.max(x_adv) < .301)
Example #19
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_generate_np_targeted_gives_adversarial_example(self): x_val = np.random.rand(10, 1000) x_val = np.array(x_val, dtype=np.float32) feed_labs = np.zeros((10, 10)) feed_labs[np.arange(10), np.random.randint(0, 9, 10)] = 1 x_adv = self.attack.generate_np(x_val, clip_min=-5., clip_max=5., y_target=feed_labs) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) worked = np.mean(np.argmax(feed_labs, axis=1) == new_labs) self.assertTrue(worked > .9)
Example #20
Source File: main.py From tensorflow-DeepFM with MIT License | 5 votes |
def _plot_fig(train_results, valid_results, model_name): colors = ["red", "blue", "green"] xs = np.arange(1, train_results.shape[1]+1) plt.figure() legends = [] for i in range(train_results.shape[0]): plt.plot(xs, train_results[i], color=colors[i], linestyle="solid", marker="o") plt.plot(xs, valid_results[i], color=colors[i], linestyle="dashed", marker="o") legends.append("train-%d"%(i+1)) legends.append("valid-%d"%(i+1)) plt.xlabel("Epoch") plt.ylabel("Normalized Gini") plt.title("%s"%model_name) plt.legend(legends) plt.savefig("./fig/%s.png"%model_name) plt.close() # load data
Example #21
Source File: chen2014.py From dustmaps with GNU General Public License v2.0 | 5 votes |
def ascii2h5(dat_fname, h5_fname): """ Converts from the original ASCII format of the Chen+ (2014) 3D dust map to the HDF5 format. Args: dat_fname (:obj:`str`): Filename of the original ASCII .dat file. h5_fname (:obj:`str`): Output filename to write the resulting HDF5 file to. """ table = np.loadtxt(dat_fname, skiprows=1, dtype='f4') filter_kwargs = dict( chunks=True, compression='gzip', compression_opts=3) # Filter out pixels with all zeros idx = ~np.all(table[:,2:32] < 1.e-5, axis=1) with h5py.File(h5_fname, 'w') as f: d = np.arange(0., 4.351, 0.15).astype('f4') dset = f.create_dataset('dists', data=d, **filter_kwargs) dset.attrs['description'] = 'Distances at which extinction is measured' dset.attrs['units'] = 'kpc' dset = f.create_dataset('pix_lb', data=table[idx,0:2], **filter_kwargs) dset.attrs['description'] = 'Galactic (l, b) of each pixel' dset.attrs['units'] = 'deg' dset = f.create_dataset('A_r', data=table[idx,2:32], **filter_kwargs) dset.attrs['description'] = 'Extinction' dset.attrs['shape'] = '(pixel, distance)' dset.attrs['band'] = 'r' dset.attrs['units'] = 'mag' dset = f.create_dataset('A_r_err', data=table[idx,32:], **filter_kwargs) dset.attrs['description'] = 'Gaussian uncertainty in extinction' dset.attrs['shape'] = '(pixel, distance)' dset.attrs['band'] = 'r' dset.attrs['units'] = 'mag'
Example #22
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 5 votes |
def create_from_hdf5(tfrecord_dir, hdf5_filename, shuffle): print('Loading HDF5 archive from "%s"' % hdf5_filename) import h5py # conda install h5py with h5py.File(hdf5_filename, 'r') as hdf5_file: hdf5_data = max([value for key, value in hdf5_file.items() if key.startswith('data')], key=lambda lod: lod.shape[3]) with TFRecordExporter(tfrecord_dir, hdf5_data.shape[0]) as tfr: order = tfr.choose_shuffled_order() if shuffle else np.arange(hdf5_data.shape[0]) for idx in range(order.size): tfr.add_image(hdf5_data[order[idx]]) npy_filename = os.path.splitext(hdf5_filename)[0] + '-labels.npy' if os.path.isfile(npy_filename): tfr.add_labels(np.load(npy_filename)[order]) #----------------------------------------------------------------------------
Example #23
Source File: test_attacks.py From neural-fingerprinting with BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_targeted_generate_np_gives_adversarial_example(self): random_labs = np.random.random_integers(0, 1, 100) random_labs_one_hot = np.zeros((100, 2)) random_labs_one_hot[np.arange(100), random_labs] = 1 _, x_adv, delta = self.generate_adversarial_examples_np( eps=.5, ord=np.inf, y_target=random_labs_one_hot) self.assertClose(delta, 0.5) new_labs = np.argmax(self.sess.run(self.model(x_adv)), axis=1) self.assertTrue(np.mean(random_labs == new_labs) > 0.7)
Example #24
Source File: input_helpers.py From deep-siamese-text-similarity with MIT License | 5 votes |
def getDataSets(self, training_paths, max_document_length, percent_dev, batch_size, is_char_based): if is_char_based: x1_text, x2_text, y=self.getTsvDataCharBased(training_paths) else: x1_text, x2_text, y=self.getTsvData(training_paths) # Build vocabulary print("Building vocabulary") vocab_processor = MyVocabularyProcessor(max_document_length,min_frequency=0,is_char_based=is_char_based) vocab_processor.fit_transform(np.concatenate((x2_text,x1_text),axis=0)) print("Length of loaded vocabulary ={}".format( len(vocab_processor.vocabulary_))) i1=0 train_set=[] dev_set=[] sum_no_of_batches = 0 x1 = np.asarray(list(vocab_processor.transform(x1_text))) x2 = np.asarray(list(vocab_processor.transform(x2_text))) # Randomly shuffle data np.random.seed(131) shuffle_indices = np.random.permutation(np.arange(len(y))) x1_shuffled = x1[shuffle_indices] x2_shuffled = x2[shuffle_indices] y_shuffled = y[shuffle_indices] dev_idx = -1*len(y_shuffled)*percent_dev//100 del x1 del x2 # Split train/test set self.dumpValidation(x1_text,x2_text,y,shuffle_indices,dev_idx,0) # TODO: This is very crude, should use cross-validation x1_train, x1_dev = x1_shuffled[:dev_idx], x1_shuffled[dev_idx:] x2_train, x2_dev = x2_shuffled[:dev_idx], x2_shuffled[dev_idx:] y_train, y_dev = y_shuffled[:dev_idx], y_shuffled[dev_idx:] print("Train/Dev split for {}: {:d}/{:d}".format(training_paths, len(y_train), len(y_dev))) sum_no_of_batches = sum_no_of_batches+(len(y_train)//batch_size) train_set=(x1_train,x2_train,y_train) dev_set=(x1_dev,x2_dev,y_dev) gc.collect() return train_set,dev_set,vocab_processor,sum_no_of_batches
Example #25
Source File: cgp_config.py From cgp-cnn with MIT License | 5 votes |
def __call__(self, net_lists): evaluations = np.zeros(len(net_lists)) for i in np.arange(0, len(net_lists), self.gpu_num): process_num = np.min((i + self.gpu_num, len(net_lists))) - i pool = mp.Pool(process_num) arg_data = [(cnn_eval, net_lists[i+j], j, self.epoch_num, self.batchsize, self.dataset, self.valid_data_ratio, self.verbose) for j in range(process_num)] evaluations[i:i+process_num] = pool.map(arg_wrapper_mp, arg_data) pool.terminate() return evaluations
Example #26
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 5 votes |
def create_cifar10(tfrecord_dir, cifar10_dir): print('Loading CIFAR-10 from "%s"' % cifar10_dir) import pickle images = [] labels = [] for batch in range(1, 6): with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file: data = pickle.load(file, encoding='latin1') images.append(data['data'].reshape(-1, 3, 32, 32)) labels.append(data['labels']) images = np.concatenate(images) labels = np.concatenate(labels) assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8 assert labels.shape == (50000,) and labels.dtype == np.int32 assert np.min(images) == 0 and np.max(images) == 255 assert np.min(labels) == 0 and np.max(labels) == 9 onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32) onehot[np.arange(labels.size), labels] = 1.0 with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr: order = tfr.choose_shuffled_order() for idx in range(order.size): tfr.add_image(images[order[idx]]) tfr.add_labels(onehot[order]) #----------------------------------------------------------------------------
Example #27
Source File: system_eq.py From BiblioPixelAnimations with MIT License | 5 votes |
def __init__(self, bins, max_freq=4000, log_scale=True, auto_gain=False, gain=3): self.max_freq = max_freq self.bins = bins self.log_scale = log_scale self.auto_gain = auto_gain self.gain = gain self.rec = Recorder(rate=self.max_freq * 2, chunksize=self.bins * 2) # computes the parameters that will be used during plotting self.freq_vect = np.fft.rfftfreq(self.rec.chunksize, 1. / self.rec.rate) self.time_vect = np.arange(self.rec.chunksize, dtype=np.float32) / self.rec.rate * 1000
Example #28
Source File: system_eq.py From BiblioPixelAnimations with MIT License | 5 votes |
def __init__(self, bins, max_freq=4000, log_scale=True, auto_gain=False, gain=3): self.max_freq = max_freq self.bins = bins self.log_scale = log_scale self.auto_gain = auto_gain self.gain = gain self.rec = Recorder(rate=self.max_freq * 2, chunksize=self.bins * 2) # computes the parameters that will be used during plotting self.freq_vect = np.fft.rfftfreq(self.rec.chunksize, 1. / self.rec.rate) self.time_vect = np.arange(self.rec.chunksize, dtype=np.float32) / self.rec.rate * 1000
Example #29
Source File: dataset_tool.py From disentangling_conditional_gans with MIT License | 5 votes |
def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order. order = np.arange(self.expected_images) np.random.RandomState(123).shuffle(order) return order
Example #30
Source File: data.py From Traffic_sign_detection_YOLO with MIT License | 5 votes |
def shuffle(self): batch = self.FLAGS.batch data = self.parse() size = len(data) print('Dataset of {} instance(s)'.format(size)) if batch > size: self.FLAGS.batch = batch = size batch_per_epoch = int(size / batch) for i in range(self.FLAGS.epoch): shuffle_idx = perm(np.arange(size)) for b in range(batch_per_epoch): # yield these x_batch = list() feed_batch = dict() for j in range(b*batch, b*batch+batch): train_instance = data[shuffle_idx[j]] try: inp, new_feed = self._batch(train_instance) except ZeroDivisionError: print("This image's width or height are zeros: ", train_instance[0]) print('train_instance:', train_instance) print('Please remove or fix it then try again.') raise if inp is None: continue x_batch += [np.expand_dims(inp, 0)] for key in new_feed: new = new_feed[key] old_feed = feed_batch.get(key, np.zeros((0,) + new.shape)) feed_batch[key] = np.concatenate([ old_feed, [new] ]) x_batch = np.concatenate(x_batch, 0) yield x_batch, feed_batch print('Finish {} epoch(es)'.format(i + 1))