SYMBOL INDEX (216 symbols across 16 files)

FILE: AD.py
  class myADclass (line 49) | class myADclass():
    method __init__ (line 50) | def __init__(self, epoch, settings=settings, samples=samples, labels=l...
    method ADfunc (line 56) | def ADfunc(self):

FILE: AD_Invert.py
  class myADclass (line 45) | class myADclass():
    method __init__ (line 46) | def __init__(self, epoch, settings=settings, samples=samples, labels=l...
    method ADfunc (line 52) | def ADfunc(self):

FILE: DR_discriminator.py
  function anomaly_detection_plot (line 10) | def anomaly_detection_plot(D_test, T_mb, L_mb, D_L, epoch, identifier):
  function detection_Comb (line 40) | def detection_Comb(Label_test, L_mb, I_mb, seq_step, tao):
  function detection_logits_I (line 87) | def detection_logits_I(DL_test, L_mb, I_mb, seq_step, tao):
  function detection_statistic_I (line 155) | def detection_statistic_I(D_test, L_mb, I_mb, seq_step, tao):
  function detection_D_I (line 222) | def detection_D_I(DD, L_mb, I_mb, seq_step, tao):
  function detection_R_D_I (line 290) | def detection_R_D_I(DD, Gs, T_mb, L_mb, seq_step, tao, lam):
  function detection_R_I (line 364) | def detection_R_I(Gs, T_mb, L_mb, seq_step, tao):
  function sample_detection (line 432) | def sample_detection(D_test, L_mb, tao):
  function CUSUM_det (line 464) | def CUSUM_det(spe_n, spe_a, labels):
  function SPE (line 531) | def SPE(X, pc):
  function generator_o (line 549) | def generator_o(z, hidden_units_g, seq_length, batch_size, num_generated...
  function discriminator_o (line 589) | def discriminator_o(x, hidden_units_d, reuse=False, parameters=None):
  function invert (line 615) | def invert(settings, samples, para_path, g_tolerance=None, e_tolerance=0.1,
  function dis_trained_model (line 722) | def dis_trained_model(settings, samples, para_path):
  function dis_D_model (line 761) | def dis_D_model(settings, samples, para_path):

FILE: data_utils.py
  function swat (line 24) | def swat(seq_length, seq_step, num_signals, randomize=False):
  function swat_birgan (line 90) | def swat_birgan(seq_length, seq_step, num_signals, randomize=False):
  function swat_test (line 133) | def swat_test(seq_length, seq_step, num_signals, randomize=False):
  function swat_birgan_test (line 200) | def swat_birgan_test(seq_length, seq_step, num_signals, randomize=False):
  function wadi (line 245) | def wadi(seq_length, seq_step, num_signals, randomize=False):
  function wadi_test (line 302) | def wadi_test(seq_length, seq_step, num_signals, randomize=False):
  function kdd99 (line 357) | def kdd99(seq_length, seq_step, num_signals):
  function kdd99_test (line 409) | def kdd99_test(seq_length, seq_step, num_signals):
  function get_samples_and_labels (line 468) | def get_samples_and_labels(settings):
  function get_data (line 544) | def get_data(data_type, seq_length, seq_step, num_signals, sub_id, eval_...
  function get_batch (line 574) | def get_batch(samples, batch_size, batch_idx, labels=None):
  function split (line 589) | def split(samples, proportions, normalise=False, scale=False, labels=Non...

FILE: differential_privacy/dp_sgd/dp_optimizer/dp_optimizer.py
  class DPGradientDescentOptimizer (line 30) | class DPGradientDescentOptimizer(tf.train.GradientDescentOptimizer):
    method __init__ (line 34) | def __init__(self, learning_rate, eps_delta, sanitizer,
    method compute_sanitized_gradients (line 72) | def compute_sanitized_gradients(self, loss, var_list=None,
    method minimize (line 110) | def minimize(self, loss, global_step=None, var_list=None,

FILE: differential_privacy/dp_sgd/dp_optimizer/sanitizer.py
  class AmortizedGaussianSanitizer (line 39) | class AmortizedGaussianSanitizer(object):
    method __init__ (line 47) | def __init__(self, accountant, default_option):
    method set_option (line 59) | def set_option(self, tensor_name, option):
    method sanitize (line 69) | def sanitize(self, x, eps_delta, sigma=None,

FILE: differential_privacy/dp_sgd/dp_optimizer/utils.py
  class LayerParameters (line 29) | class LayerParameters(object):
    method __init__ (line 31) | def __init__(self):
  class ConvParameters (line 42) | class ConvParameters(object):
    method __init__ (line 44) | def __init__(self):
  class NetworkParameters (line 62) | class NetworkParameters(object):
    method __init__ (line 64) | def __init__(self):
  function GetTensorOpName (line 73) | def GetTensorOpName(x):
  function BuildNetwork (line 91) | def BuildNetwork(inputs, network_parameters):
  function VaryRate (line 199) | def VaryRate(start, end, saturate_epochs, epoch):
  function BatchClipByL2norm (line 223) | def BatchClipByL2norm(t, upper_bound, name=None):
  function SoftThreshold (line 254) | def SoftThreshold(t, threshold_ratio, name=None):
  function AddGaussianNoise (line 283) | def AddGaussianNoise(t, sigma, name=None):
  function GenerateBinomialTable (line 300) | def GenerateBinomialTable(m):

FILE: differential_privacy/privacy_accountant/tf/accountant.py
  class AmortizedAccountant (line 50) | class AmortizedAccountant(object):
    method __init__ (line 59) | def __init__(self, total_examples):
    method accumulate_privacy_spending (line 73) | def accumulate_privacy_spending(self, eps_delta, unused_sigma,
    method get_privacy_spent (line 108) | def get_privacy_spent(self, sess, target_eps=None):
  class MomentsAccountant (line 127) | class MomentsAccountant(object):
    method __init__ (line 179) | def __init__(self, total_examples, moment_orders=32):
    method _compute_log_moment (line 200) | def _compute_log_moment(self, sigma, q, moment_order):
    method accumulate_privacy_spending (line 212) | def accumulate_privacy_spending(self, unused_eps_delta,
    method _compute_delta (line 241) | def _compute_delta(self, log_moments, eps):
    method _compute_eps (line 261) | def _compute_eps(self, log_moments, delta):
    method get_privacy_spent (line 270) | def get_privacy_spent(self, sess, target_eps=None, target_deltas=None):
  class GaussianMomentsAccountant (line 299) | class GaussianMomentsAccountant(MomentsAccountant):
    method __init__ (line 332) | def __init__(self, total_examples, moment_orders=32):
    method _differential_moments (line 342) | def _differential_moments(self, sigma, s, t):
    method _compute_log_moment (line 379) | def _compute_log_moment(self, sigma, q, moment_order):
  class DummyAccountant (line 407) | class DummyAccountant(object):
    method accumulate_privacy_spending (line 410) | def accumulate_privacy_spending(self, *unused_args):
    method get_privacy_spent (line 413) | def get_privacy_spent(self, unused_sess, **unused_kwargs):

FILE: eugenium_mmd.py
  function my_kernel (line 21) | def my_kernel(X, Y, sigma):
  function MMD_3_Sample_Test (line 36) | def MMD_3_Sample_Test(X, Y, Z, sigma=-1, SelectSigma=True, computeMMDs=F...
  function MMD_Diff_Var (line 92) | def MMD_Diff_Var(Kyy, Kzz, Kxy, Kxz):
  function grbf (line 151) | def grbf(x1, x2, sigma):
  function kernelwidthPair (line 174) | def kernelwidthPair(x1, x2):
  function kernelwidth (line 204) | def kernelwidth(Zmed):
  function MMD_unbiased (line 229) | def MMD_unbiased(Kxx, Kyy, Kxy):

FILE: eval.py
  function assert_same_data (line 33) | def assert_same_data(A, B):
  function model_memorisation (line 48) | def model_memorisation(identifier, epoch, max_samples=2000, tstr=False):
  function model_comparison (line 128) | def model_comparison(identifier_A, identifier_B, epoch_A=99, epoch_B=99):
  function get_reconstruction_errors (line 160) | def get_reconstruction_errors(identifier, epoch, g_tolerance=0.05, max_s...
  function error_per_sample (line 261) | def error_per_sample(identifier, epoch, samples, n_rep=3, n_iter=None, g...
  function view_digit (line 281) | def view_digit(identifier, epoch, digit, n_samples=6):
  function view_interpolation (line 301) | def view_interpolation(identifier, epoch, n_steps=6, input_samples=None,...
  function view_latent_vary (line 332) | def view_latent_vary(identifier, epoch, n_steps=6):
  function view_reconstruction (line 343) | def view_reconstruction(identifier, epoch, real_samples, tolerance=1):
  function view_fixed (line 356) | def view_fixed(identifier, epoch, n_samples=6, dim=None):
  function view_params (line 373) | def view_params(identifier, epoch):
  function sample_distance (line 382) | def sample_distance(sampleA, sampleB, sigma):
  function train_CNN (line 394) | def train_CNN(train_X, train_Y, vali_X, vali_Y, test_X):
  function TSTR_mnist (line 432) | def TSTR_mnist(identifier, epoch, generate=True, duplicate_synth=1, vali...
  function TSTR_eICU (line 542) | def TSTR_eICU(identifier, epoch, generate=True, vali=True, CNN=False, do...
  function NIPS_toy_plot (line 682) | def NIPS_toy_plot(identifier_rbf, epoch_rbf, identifier_sine, epoch_sine...

FILE: mmd.py
  function _mix_rbf_kernel (line 21) | def _mix_rbf_kernel(X, Y, sigmas, wts=None):
  function rbf_mmd2 (line 57) | def rbf_mmd2(X, Y, sigma=1, biased=True):
  function mix_rbf_mmd2 (line 61) | def mix_rbf_mmd2(X, Y, sigmas=(1,), wts=None, biased=True):
  function rbf_mmd2_and_ratio (line 66) | def rbf_mmd2_and_ratio(X, Y, sigma=1, biased=True):
  function mix_rbf_mmd2_and_ratio (line 70) | def mix_rbf_mmd2_and_ratio(X, Y, sigmas=(1,), wts=None, biased=True):
  function _mmd2 (line 79) | def _mmd2(K_XX, K_XY, K_YY, const_diagonal=False, biased=False):
  function _mmd2_and_ratio (line 102) | def _mmd2_and_ratio(K_XX, K_XY, K_YY, const_diagonal=False, biased=False,
  function _mmd2_and_variance (line 110) | def _mmd2_and_variance(K_XX, K_XY, K_YY, const_diagonal=False, biased=Fa...
  function median_pairwise_distance (line 172) | def median_pairwise_distance(X, Y=None):
  function median_pairwise_distance_o (line 203) | def median_pairwise_distance_o(X, Y=None):

FILE: mod_core_rnn_cell_impl.py
  function _checked_scope (line 57) | def _checked_scope(cell, scope, reuse=None, **kwargs):
  class BasicRNNCell (line 103) | class BasicRNNCell(RNNCell):
    method __init__ (line 106) | def __init__(self, num_units, input_size=None, activation=tanh, reuse=...
    method state_size (line 114) | def state_size(self):
    method output_size (line 118) | def output_size(self):
    method __call__ (line 121) | def __call__(self, inputs, state, scope=None):
  class GRUCell (line 129) | class GRUCell(RNNCell):
    method __init__ (line 132) | def __init__(self, num_units, input_size=None, activation=tanh, reuse=...
    method state_size (line 140) | def state_size(self):
    method output_size (line 144) | def output_size(self):
    method __call__ (line 147) | def __call__(self, inputs, state, scope=None):
  class LSTMStateTuple (line 168) | class LSTMStateTuple(_LSTMStateTuple):
    method dtype (line 178) | def dtype(self):
  class BasicLSTMCell (line 186) | class BasicLSTMCell(RNNCell):
    method __init__ (line 200) | def __init__(self, num_units, forget_bias=1.0, input_size=None,
    method state_size (line 228) | def state_size(self):
    method output_size (line 233) | def output_size(self):
    method __call__ (line 236) | def __call__(self, inputs, state, scope=None):
  class LSTMCell (line 260) | class LSTMCell(RNNCell):
    method __init__ (line 282) | def __init__(self, num_units, input_size=None,
    method state_size (line 357) | def state_size(self):
    method output_size (line 361) | def output_size(self):
    method __call__ (line 364) | def __call__(self, inputs, state, scope=None):
  class OutputProjectionWrapper (line 459) | class OutputProjectionWrapper(RNNCell):
    method __init__ (line 468) | def __init__(self, cell, output_size, reuse=None):
    method state_size (line 491) | def state_size(self):
    method output_size (line 495) | def output_size(self):
    method zero_state (line 498) | def zero_state(self, batch_size, dtype):
    method __call__ (line 502) | def __call__(self, inputs, state, scope=None):
  class InputProjectionWrapper (line 512) | class InputProjectionWrapper(RNNCell):
    method __init__ (line 520) | def __init__(self, cell, num_proj, input_size=None):
    method state_size (line 539) | def state_size(self):
    method output_size (line 543) | def output_size(self):
    method zero_state (line 546) | def zero_state(self, batch_size, dtype):
    method __call__ (line 550) | def __call__(self, inputs, state, scope=None):
  function _enumerated_map_structure (line 558) | def _enumerated_map_structure(map_fn, *args, **kwargs):
  class DropoutWrapper (line 567) | class DropoutWrapper(RNNCell):
    method __init__ (line 570) | def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
    method _gen_seed (line 667) | def _gen_seed(self, salt_prefix, index):
    method state_size (line 675) | def state_size(self):
    method output_size (line 679) | def output_size(self):
    method zero_state (line 682) | def zero_state(self, batch_size, dtype):
    method _variational_recurrent_dropout_value (line 686) | def _variational_recurrent_dropout_value(
    method _dropout (line 698) | def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
    method __call__ (line 710) | def __call__(self, inputs, state, scope=None):
  class ResidualWrapper (line 731) | class ResidualWrapper(RNNCell):
    method __init__ (line 734) | def __init__(self, cell):
    method state_size (line 743) | def state_size(self):
    method output_size (line 747) | def output_size(self):
    method zero_state (line 750) | def zero_state(self, batch_size, dtype):
    method __call__ (line 754) | def __call__(self, inputs, state, scope=None):
  class DeviceWrapper (line 780) | class DeviceWrapper(RNNCell):
    method __init__ (line 783) | def __init__(self, cell, device):
    method state_size (line 796) | def state_size(self):
    method output_size (line 800) | def output_size(self):
    method zero_state (line 803) | def zero_state(self, batch_size, dtype):
    method __call__ (line 807) | def __call__(self, inputs, state, scope=None):
  class EmbeddingWrapper (line 813) | class EmbeddingWrapper(RNNCell):
    method __init__ (line 822) | def __init__(self, cell, embedding_classes, embedding_size, initialize...
    method state_size (line 852) | def state_size(self):
    method output_size (line 856) | def output_size(self):
    method zero_state (line 859) | def zero_state(self, batch_size, dtype):
    method __call__ (line 863) | def __call__(self, inputs, state, scope=None):
  class MultiRNNCell (line 890) | class MultiRNNCell(RNNCell):
    method __init__ (line 893) | def __init__(self, cells, state_is_tuple=True):
    method state_size (line 922) | def state_size(self):
    method output_size (line 929) | def output_size(self):
    method zero_state (line 932) | def zero_state(self, batch_size, dtype):
    method __call__ (line 941) | def __call__(self, inputs, state, scope=None):
  class _SlimRNNCell (line 966) | class _SlimRNNCell(RNNCell):
    method __init__ (line 969) | def __init__(self, cell_fn):
    method state_size (line 998) | def state_size(self):
    method output_size (line 1002) | def output_size(self):
    method __call__ (line 1005) | def __call__(self, inputs, state, scope=None):
  function _linear (line 1011) | def _linear(args, output_size, bias, bias_start=0.0, scope=None):

FILE: model.py
  function sample_Z (line 25) | def sample_Z(batch_size, seq_length, latent_dim, use_time=False, use_noi...
  function sample_T (line 34) | def sample_T(batch_size, batch_idx):
  function sample_TT (line 46) | def sample_TT(batch_size):
  function train_epoch (line 57) | def train_epoch(epoch, samples, labels, sess, Z, X, D_loss, G_loss, D_so...
  function GAN_loss (line 88) | def GAN_loss(Z, X, generator_settings, discriminator_settings):
  function GAN_solvers (line 114) | def GAN_solvers(D_loss, G_loss, learning_rate, batch_size, total_example...
  function create_placeholders (line 150) | def create_placeholders(batch_size, seq_length, latent_dim, num_signals):
  function generator (line 157) | def generator(z, hidden_units_g, seq_length, batch_size, num_signals, re...
  function discriminator (line 209) | def discriminator(x, hidden_units_d, seq_length, batch_size, reuse=False...
  function display_batch_progression (line 250) | def display_batch_progression(j, id_max):
  function dump_parameters (line 261) | def dump_parameters(identifier, sess):
  function load_parameters (line 275) | def load_parameters(identifier):

FILE: plotting.py
  function plot_label (line 10) | def plot_label(label, id):
  function visualise_at_epoch (line 20) | def visualise_at_epoch(vis_sample, data, predict_labels, epoch,
  function save_plot_sample (line 48) | def save_plot_sample(samples, idx, identifier, n_samples=16, num_epochs=...
  function save_plot_interpolate (line 77) | def save_plot_interpolate(input_samples, samples, idx, identifier,  num_...
  function reconstruction_errors (line 130) | def reconstruction_errors(identifier, train_errors, vali_errors,
  function save_plot_reconstruct (line 158) | def save_plot_reconstruct(real_samples, model_samples, identifier):
  function save_plot_vary_dimension (line 180) | def save_plot_vary_dimension(samples_list, idx, identifier, n_dim):
  function interpolate (line 213) | def interpolate(sampleA, sampleB=None, n_steps=6):
  function vary_latent_dimension (line 224) | def vary_latent_dimension(sample, dimension, n_steps=6):
  function plot_sine_evaluation (line 235) | def plot_sine_evaluation(real_samples, fake_samples, idx, identifier):
  function plot_trace (line 270) | def plot_trace(identifier, xmax=250, final=False, dp=False):
  function save_samples (line 386) | def save_samples(vis_sample, identifier, epoch):
  function save_samples_real (line 392) | def save_samples_real(vis_real, identifier):
  function save_mnist_plot_sample (line 398) | def save_mnist_plot_sample(samples, idx, identifier, n_samples, labels=N...
  function visualise_latent (line 436) | def visualise_latent(Z, identifier):
  function plot_parameters (line 456) | def plot_parameters(parameters, identifier):
  function view_mnist_eval (line 491) | def view_mnist_eval(identifier, train_X, train_Y, synth_X, synth_Y, test...
  function nips_plot_rbf (line 544) | def nips_plot_rbf(sample, index, which='train'):
  function nips_plot_sine (line 570) | def nips_plot_sine(sample, index, which='train'):
  function nips_plot_mnist (line 599) | def nips_plot_mnist(sample, index, which='train'):

FILE: tf_ops.py
  function sq_sum (line 5) | def sq_sum(t, name=None):
  function dot (line 12) | def dot(x, y, name=None):

FILE: utils.py
  function rgan_options_parser (line 6) | def rgan_options_parser():
  function load_settings_from_file (line 93) | def load_settings_from_file(settings):