我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用scipy.stats.truncnorm()。
def __init__(self, img_feat, tags_idx, a_tags_idx, test_tags_idx, z_dim, vocab_processor): self.z_sampler = stats.truncnorm((-1 - 0.) / 1., (1 - 0.) / 1., loc=0., scale=1) self.length = len(tags_idx) self.current = 0 self.img_feat = img_feat self.tags_idx = tags_idx self.a_tags_idx = a_tags_idx self.w_idx = np.arange(self.length) self.w_idx2 = np.arange(self.length) self.tmp = 0 self.epoch = 0 self.vocab_processor = vocab_processor self.vocab_size = len(vocab_processor._reverse_mapping) self.unk_id = vocab_processor._mapping['<UNK>'] self.eos_id = vocab_processor._mapping['<EOS>'] self.hair_id = vocab_processor._mapping['hair'] self.eyes_id = vocab_processor._mapping['eyes'] self.gen_info() self.test_tags_idx = self.gen_test_hot(test_tags_idx) self.fixed_z = self.next_noise_batch(len(self.test_tags_idx), z_dim) idx = np.random.permutation(np.arange(self.length)) self.w_idx2 = self.w_idx2[idx]
def __init__(self, num_classes=1000, aux_logits=True, transform_input=False): super(Inception3, self).__init__() self.aux_logits = aux_logits self.transform_input = transform_input self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) if aux_logits: self.AuxLogits = InceptionAux(768, num_classes) self.Mixed_7a = InceptionD(768) self.Mixed_7b = InceptionE(1280) self.Mixed_7c = InceptionE(2048) self.group1 = nn.Sequential( OrderedDict([ ('fc', nn.Linear(2048, num_classes)) ]) ) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): import scipy.stats as stats stddev = m.stddev if hasattr(m, 'stddev') else 0.1 X = stats.truncnorm(-2, 2, scale=stddev) values = torch.Tensor(X.rvs(m.weight.data.numel())) m.weight.data.copy_(values) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
def test_truncnorm_prior(self): """check truncnorm RV""" msg = "truncnorm prior: incorrect test {0}" test_vals = [-0.1, 0.0, 0.5, 1.0, 1.1] mean, std, low, up = 0.9, 0.1, 0.0, 1.0 a, b = (low - mean) / std, (up - mean) / std correct = truncnorm(loc=mean, scale=std, a=a, b=b) for ii, xx in enumerate(test_vals): assert self.gPrior.pdf(xx) == correct.pdf(xx), msg.format(ii)
def randomGammaCorrection(image): lower = 0.5 upper = 1.5 mu = 1 sigma = 0.5 alpha = stats.truncnorm((lower-mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma) image = (pow(image/255.0, alpha.rvs(1)[0]) * 255).astype(np.uint8) return image # def rle(img): # ''' # img: numpy array, 1 - mask, 0 - background # Returns run length as string formated # ''' # bytes = np.where(img.flatten() == 1)[0] # runs = [] # prev = -2 # for b in bytes: # if (b > prev + 1): runs.extend((b + 1, 0)) # runs[-1] += 1 # prev = b # # return ' '.join([str(i) for i in runs]) # https://www.kaggle.com/stainsby/fast-tested-rle
def __init__(self, num_classes=1000, aux_logits=True, transform_input=False): super(Inception3, self).__init__() self.aux_logits = aux_logits self.transform_input = transform_input self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) if aux_logits: self.AuxLogits = InceptionAux(768, num_classes) self.Mixed_7a = InceptionD(768) self.Mixed_7b = InceptionE(1280) self.Mixed_7c = InceptionE(2048) self.fc = nn.Linear(2048, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): import scipy.stats as stats stddev = m.stddev if hasattr(m, 'stddev') else 0.1 X = stats.truncnorm(-2, 2, scale=stddev) values = torch.Tensor(X.rvs(m.weight.data.numel())) m.weight.data.copy_(values) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
def __init__(self, num_classes=1000, aux_logits=True, transform_input=False): super(Inception3, self).__init__() self.aux_logits = aux_logits self.transform_input = transform_input self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) self.maxPool1 = nn.MaxPool2d(kernel_size=3, stride=2) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) self.maxPool2 = nn.MaxPool2d(kernel_size=3, stride=2) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) if aux_logits: self.AuxLogits = InceptionAux(768, num_classes) self.Mixed_7a = InceptionD(768) self.Mixed_7b = InceptionE(1280) self.Mixed_7c = InceptionE(2048) self.avgpool = nn.AvgPool2d(kernel_size=8) self.dropout = nn.Dropout() self.fc = nn.Linear(2048, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): import scipy.stats as stats stddev = m.stddev if hasattr(m, 'stddev') else 0.1 X = stats.truncnorm(-2, 2, scale=stddev) values = torch.Tensor(X.rvs(m.weight.data.numel())) m.weight.data.copy_(values) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
def GaussianDistribution(mean, std, a=None, b=None): if std == .0: return Distribution.ConstantDistribution(mean) if a is None and b is None: # Unbounded Gaussian. return N(mean, std) else: dist = UncertainVariable(ss.truncnorm( a = -np.inf if a is None else (a - mean) / std, b = np.inf if b is None else (b - mean) / std, loc = mean, scale = std)) logging.debug('Distribution -- truncated gaussian: {}, {} [{}, {}]'.format( dist.mean, np.sqrt(dist.var), a, b)) return dist
def __init__(self, num_classes=None, aux_logits=True, transform_input=False): super(Inception3, self).__init__() self.aux_logits = aux_logits self.transform_input = transform_input self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2) self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3) self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1) self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1) self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3) self.Mixed_5b = InceptionA(192, pool_features=32) self.Mixed_5c = InceptionA(256, pool_features=64) self.Mixed_5d = InceptionA(288, pool_features=64) self.Mixed_6a = InceptionB(288) self.Mixed_6b = InceptionC(768, channels_7x7=128) self.Mixed_6c = InceptionC(768, channels_7x7=160) self.Mixed_6d = InceptionC(768, channels_7x7=160) self.Mixed_6e = InceptionC(768, channels_7x7=192) self.num_classes = num_classes if self.num_classes is not None: if aux_logits: self.AuxLogits = InceptionAux(768, num_classes) self.Mixed_7a = InceptionD(768) self.Mixed_7b = InceptionE(1280) self.Mixed_7c = InceptionE(2048) self.fc = nn.Linear(2048, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): import scipy.stats as stats stddev = m.stddev if hasattr(m, 'stddev') else 0.1 X = stats.truncnorm(-2, 2, scale=stddev) values = torch.Tensor(X.rvs(m.weight.data.numel())) m.weight.data.copy_(values) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_()
def get_model(n_obs=50, true_params=None, seed_obs=None, stochastic=True): """Return a complete Ricker model in inference task. This is a simplified example that achieves reasonable predictions. For more extensive treatment and description using 13 summary statistics, see: Wood, S. N. (2010) Statistical inference for noisy nonlinear ecological dynamic systems, Nature 466, 1102–1107. Parameters ---------- n_obs : int, optional Number of observations. true_params : list, optional Parameters with which the observed data is generated. seed_obs : int, optional Seed for the observed data generation. stochastic : bool, optional Whether to use the stochastic or deterministic Ricker model. Returns ------- m : elfi.ElfiModel """ if stochastic: simulator = partial(stochastic_ricker, n_obs=n_obs) if true_params is None: true_params = [3.8, 0.3, 10.] else: simulator = partial(ricker, n_obs=n_obs) if true_params is None: true_params = [3.8] m = elfi.ElfiModel() y_obs = simulator(*true_params, n_obs=n_obs, random_state=np.random.RandomState(seed_obs)) sim_fn = partial(simulator, n_obs=n_obs) sumstats = [] if stochastic: elfi.Prior(ss.expon, np.e, 2, model=m, name='t1') elfi.Prior(ss.truncnorm, 0, 5, model=m, name='t2') elfi.Prior(ss.uniform, 0, 100, model=m, name='t3') elfi.Simulator(sim_fn, m['t1'], m['t2'], m['t3'], observed=y_obs, name='Ricker') sumstats.append(elfi.Summary(partial(np.mean, axis=1), m['Ricker'], name='Mean')) sumstats.append(elfi.Summary(partial(np.var, axis=1), m['Ricker'], name='Var')) sumstats.append(elfi.Summary(num_zeros, m['Ricker'], name='#0')) elfi.Discrepancy(chi_squared, *sumstats, name='d') else: # very simple deterministic case elfi.Prior(ss.expon, np.e, model=m, name='t1') elfi.Simulator(sim_fn, m['t1'], observed=y_obs, name='Ricker') sumstats.append(elfi.Summary(partial(np.mean, axis=1), m['Ricker'], name='Mean')) elfi.Distance('euclidean', *sumstats, name='d') return m
