我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用numpy.shuffle()。
def _get_colors(self, f): ''' Misterious function, ask @inconvergent :) ''' scale = 1./255. im = Image.open(f) w, h = im.size rgbim = im.convert('RGB') res = [] for i in xrange(0, w): for j in xrange(0, h): r, g, b = rgbim.getpixel((i, j)) res.append((r*scale, g*scale, b*scale)) np.shuffle(res) self.colors = res self.n_colors = len(res)
def __next__(self): if self._train_location >= len(self._train_indices) or self._test_location >= len(self._test_indices): # Reset: np.shuffle(self._train_indices) np.shuffle(self._test_indices) self._train_location = 0 self._test_location = 0 raise StopIteration() return self.get_train_test_batch()
def sample(self, n=1): counts = np.random.multinomial(n, self.weights, size=1) samples = np.empty((n, len(self.means[0]))) k = 0 for i in range(len(self.means)): for j in range(len(self.means[i])): samples[k:k+counts[i], j] = stats.norm.rvs(loc=self.means[i, j], scale=self.stds[i, j], size=counts[i]) k += counts[i] np.shuffle(samples) return samples
def sample(self, n=1): counts = np.random.multinomial(n, self.weights, size=1) samples = np.empty((n, len(self.means[0]))) j = 0 for i in range(len(self.means)): samples[j:j+counts[i]] = stats.multivariate_normal.rvs(mean=self.means[i], cov=self.covs[i], size=counts[i]) j += counts[i] np.shuffle(samples) return samples
def _split_into_groups(iterable, ngroups=-1, fractions=None, shuffle=True): if shuffle: iterable = np.copy(iterable) np.shuffle(iterable) start_idxs, end_idxs = _group_start_end_idxs(len(iterable), ngroups, fractions) return [iterable[start:end] for start, end in zip(start_idxs, end_idxs)]
def __init__(self, hdf_filename, test_pct=0.25, neg_bias=0.5, batch_size=64, normalize=False, malignancy_to_class=None, window_normalize=False): neg_bias = 0.5 if neg_bias is None else neg_bias self._hdf_filename = hdf_filename self._neg_bias = neg_bias self._test_pct = test_pct self._batch_size = batch_size self._test_location = 0 self._train_location = 0 self._test_indices = [] self._train_indices = [] self._malignancy_to_class = malignancy_to_class self._normalize = normalize self._Xmin = None self._Xmax = None self._window_normalize = window_normalize if malignancy_to_class is not None and len(malignancy_to_class) != 6: raise Exception("malignancy_class mapping must contain exactly 6 values, one for each malignancy level 0 - 5") # Open the hdf file self._hdf_file = h5py.File(self._hdf_filename, 'r') # Get info on classes and makeup of the dataset by examining the y values (classes): y = self._hdf_file['nodule_classes'].value if self._malignancy_to_class is not None: if malignancy_to_class is not None: mal = self._hdf_file['nodule_malignancy'] for i in range(len(y)): y[i] = [malignancy_to_class[int(mal[i])]] if self._normalize: self._Xmin = self._hdf_file['nodule_pixel_min'] self._Xmax = self._hdf_file['nodule_pixel_max'] n_examples = len(y) negatives = [i for i in range(n_examples) if y[i] == [0]] positives = [i for i in range(n_examples) if y[i][0] > 0] neg_count = len(negatives) pos_count = len(positives) n_examples = neg_count + pos_count neg_goal = int(min(neg_count, round(neg_bias * n_examples))) pos_goal = n_examples - neg_goal if pos_goal > pos_count: neg_goal = int(round(pos_count * (1-neg_bias+0.5))) pos_goal = pos_count # print("Before: neg count: {0}; goal: {1} - pos count: {2}; goal: {3}".format(neg_count, neg_goal, pos_count, pos_goal)) # randomly choose neg_goal negatives and pos_goal positives: selected_indices = list(np.random.choice(negatives, size=(min(neg_count,neg_goal)), replace=False)) selected_indices.extend(list(np.random.choice(positives, size=(min(pos_count, pos_goal)), replace=False))) # print("n examples: {0}".format(len(selected_indices))) n_examples = len(selected_indices) np.random.shuffle(selected_indices) test_examples = int(round(test_pct * n_examples)) # print("test_examples: {0}".format(test_examples)) self._test_indices = selected_indices[0:test_examples] self._train_indices = selected_indices[test_examples:]
