Activations for models.
Copied over from Keras.
deepchem.nn.activations.
get_from_module
(identifier, module_params, module_name, instantiate=False, kwargs=None)[source]¶Retrieves a class of function member of a module.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | The target object. |
| Raises: | ValueError: if the identifier cannot be found. |
Place constraints on models.
deepchem.nn.constraints.
MaxNorm
(m=2, axis=0)[source]¶Bases: deepchem.nn.constraints.Constraint
MaxNorm weight constraint.
Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.
| Parameters: |
|
|---|
deepchem.nn.constraints.
NonNeg
[source]¶Bases: deepchem.nn.constraints.Constraint
Constrains the weights to be non-negative.
deepchem.nn.constraints.
UnitNorm
(axis=0)[source]¶Bases: deepchem.nn.constraints.Constraint
Constrains the weights incident to each hidden unit to have unit norm.
Copies Classes from keras to remove dependency.
Most of this code is copied over from Keras. Hoping to use as a staging area while we remove our Keras dependency.
deepchem.nn.copy.
BatchNormalization
(epsilon=0.001, mode=0, axis=-1, momentum=0.99, beta_init='zero', gamma_init='one', gamma_regularizer=None, beta_regularizer=None, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Batch normalization layer (Ioffe and Szegedy, 2014).
Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
| Parameters: |
|
|---|
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.copy.
Dense
(output_dim, input_dim, init='glorot_uniform', activation='relu', bias=True, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Just your regular densely-connected NN layer.
TODO(rbharath): Make this functional in deepchem
Example:
>>> import deepchem as dc
>>> # as first layer in a sequential model:
>>> model = dc.models.Sequential()
>>> model.add(dc.nn.Input(shape=16))
>>> model.add(dc.nn.Dense(32))
>>> # now the model will take as input arrays of shape (*, 16)
>>> # and output arrays of shape (*, 32)
>>> # this is equivalent to the above:
>>> model = dc.models.Sequential()
>>> model.add(dc.nn.Input(shape=16))
>>> model.add(dc.nn.Dense(32))
| Parameters: |
|
|---|
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x)¶This is where the layer’s logic lives.
| Parameters: | x (input tensor, or list/tuple of input tensors.) – |
|---|---|
| Returns: | |
| Return type: | A tensor or list/tuple of tensors. |
deepchem.nn.copy.
Dropout
(p, seed=None, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Applies Dropout to the input.
Dropout consists in randomly setting a fraction p of input units to 0 at each update during training time, which helps prevent overfitting.
| Parameters: |
|
|---|
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.copy.
Input
(shape=None, batch_shape=None, name=None, dtype=tf.float32)[source]¶Input() is used to create a placeholder input
| Parameters: |
|
|---|
deepchem.nn.copy.
InputLayer
(input_shape=None, batch_input_shape=None, input_dtype=None, name=None)[source]¶Bases: deepchem.nn.copy.Layer
Layer to be used as an entry point into a graph.
Create its a placeholder tensor (pass arguments input_shape or batch_input_shape as well as input_dtype).
| Parameters: |
|
|---|
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x)¶This is where the layer’s logic lives.
| Parameters: | x (input tensor, or list/tuple of input tensors.) – |
|---|---|
| Returns: | |
| Return type: | A tensor or list/tuple of tensors. |
deepchem.nn.copy.
Layer
(**kwargs)[source]¶Bases: object
Abstract base layer class.
name
¶String, must be unique within a model.
trainable
¶Boolean, whether the layer weights
will be updated during training.
uses_learning_phase
¶Whether any operation
of the layer uses model_ops.in_training_phase() or model_ops.in_test_phase().
input_shape
¶Shape tuple. Provided for convenience,
but note that there may be cases in which this attribute is ill-defined (e.g. a shared layer with multiple input shapes), in which case requesting input_shape will raise an Exception. Prefer using layer.get_input_shape_for(input_shape),
output_shape
¶Shape tuple. See above.
input, output
Input/output tensor(s). Note that if the layer is used
more than once (shared layer), this is ill-defined and will raise an exception. In such cases, use
call(x): Where the layer's logic lives.
__call__(x): Wrapper around the layer logic (`call`).
If layer is not built:
__call__
(x)[source]¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
Ops for tensor initialization
deepchem.nn.initializations.
glorot_normal
(shape, name=None)[source]¶Glorot normal variance scaling initializer.
deepchem.nn.initializations.
he_normal
(shape, name=None)[source]¶He normal variance scaling initializer.
deepchem.nn.initializations.
he_uniform
(shape, name=None)[source]¶He uniform variance scaling initializer.
deepchem.nn.initializations.
lecun_uniform
(shape, name=None)[source]¶LeCun uniform variance scaling initializer.
deepchem.nn.initializations.
orthogonal
(shape, scale=1.1, name=None)[source]¶Orthogonal initializer.
Custom Keras Layers.
deepchem.nn.layers.
AttnLSTMEmbedding
(n_test, n_support, n_feat, max_depth, init='glorot_uniform', activation='linear', dropout=None, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Implements AttnLSTM as in matching networks paper.
References: Matching Networks for One Shot Learning https://arxiv.org/pdf/1606.04080v1.pdf
Order Matters: Sequence to sequence for sets https://arxiv.org/abs/1511.06391
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x_xp, mask=None)[source]¶Execute this layer on input tensors.
| Parameters: | x_xp (list) – List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and Xp should be of shape (n_support, n_feat) where n_test is the size of the test set, n_support that of the support set, and n_feat is the number of per-atom features. |
|---|---|
| Returns: | Returns two tensors of same shape as input. Namely the output shape will be [(n_test, n_feat), (n_support, n_feat)] |
| Return type: | list |
deepchem.nn.layers.
DAGGather
(n_graph_feat=30, n_outputs=30, max_atoms=50, layer_sizes=[100], init='glorot_uniform', activation='relu', dropout=None, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Gather layer of DAG model for each molecule, graph outputs are summed and input into another NN
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.layers.
DAGLayer
(n_graph_feat=30, n_atom_feat=75, max_atoms=50, layer_sizes=[100], init='glorot_uniform', activation='relu', dropout=None, batch_size=64, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
” Main layer of DAG model For a molecule with n atoms, n different graphs are generated and run through The final outputs of each graph become the graph features of corresponding atom, which will be summed and put into another network in DAGGather Layer
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x, mask=None)[source]¶Execute this layer on input tensors.
x = [atom_features, parents, calculation_orders, calculation_masks, membership, n_atoms]
| Parameters: |
|
|---|---|
| Returns: | outputs – Tensor of atom features, of shape (n_atoms, n_graph_feat) |
| Return type: | tf.Tensor |
deepchem.nn.layers.
DTNNEmbedding
(n_embedding=30, periodic_table_length=30, init='glorot_uniform', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Generate embeddings for all atoms in the batch
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.layers.
DTNNGather
(n_embedding=30, n_outputs=100, layer_sizes=[100], output_activation=True, init='glorot_uniform', activation='tanh', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Map the atomic features into molecular properties and sum
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.layers.
DTNNStep
(n_embedding=30, n_distance=100, n_hidden=60, init='glorot_uniform', activation='tanh', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
A convolution step that merge in distance and atom info of all other atoms into current atom.
model based on https://arxiv.org/abs/1609.08259
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x)[source]¶Execute this layer on input tensors.
| Parameters: | x (list of Tensor) – should be [atom_features: n_atoms*n_embedding, distance_matrix: n_pairs*n_distance, atom_membership: n_atoms distance_membership_i: n_pairs, distance_membership_j: n_pairs, ] |
|---|---|
| Returns: | new embeddings for atoms, same shape as x[0] |
| Return type: | tf.Tensor |
deepchem.nn.layers.
GraphConv
(nb_filter, n_atom_features, init='glorot_uniform', activation='linear', dropout=None, max_deg=10, min_deg=0, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
“Performs a graph convolution.
Note this layer expects the presence of placeholders defined by GraphTopology and expects that they follow the ordering provided by GraphTopology.get_input_placeholders().
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
build
()[source]¶“Construct internal trainable weights.
n_atom_features should provide the number of features per atom.
| Parameters: | n_atom_features (int) – Number of features provied per atom. |
|---|
call
(x, mask=None)[source]¶Execute this layer on input tensors.
This layer is meant to be executed on a Graph. So x is expected to be a list of placeholders, with the first placeholder the list of atom_features (learned or input) at this level, the second the deg_slice, the third the membership, and the remaining the deg_adj_lists.
Visually
x = [atom_features, deg_slice, membership, deg_adj_list placeholders...]
| Parameters: |
|
|---|---|
| Returns: | atom_features – Of shape (n_atoms, nb_filter) |
| Return type: | tf.Tensor |
deepchem.nn.layers.
GraphGather
(batch_size, activation='linear', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Gathers information for each molecule.
The various graph convolution operations expect as input a tensor atom_features of shape (n_atoms, n_feat). However, we train on batches of molecules at a time. The GraphTopology object groups a list of molecules into the atom_features tensor. The tensorial operations are done on this tensor, but at the end, the atoms need to be grouped back into molecules. This layer takes care of that operation.
Note this layer expects the presence of placeholders defined by GraphTopology and expects that they follow the ordering provided by GraphTopology.get_input_placeholders().
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x, mask=None)[source]¶Execute this layer on input tensors.
This layer is meant to be executed on a Graph. So x is expected to be a list of placeholders, with the first placeholder the list of atom_features (learned or input) at this level, the second the deg_slice, the third the membership, and the remaining the deg_adj_lists.
Visually
x = [atom_features, deg_slice, membership, deg_adj_list placeholders...]
| Parameters: |
|
|---|---|
| Returns: | Of shape (batch_size, n_feat), where n_feat is number of atom_features |
| Return type: | tf.Tensor |
deepchem.nn.layers.
GraphPool
(max_deg=10, min_deg=0, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Performs a pooling operation over an arbitrary graph.
Performs a max pool over the feature vectors for an atom and its neighbors in bond-graph. Returns a tensor of the same size as the input.
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x, mask=None)[source]¶Execute this layer on input tensors.
This layer is meant to be executed on a Graph. So x is expected to be a list of placeholders, with the first placeholder the list of atom_features (learned or input) at this level, the second the deg_slice, the third the membership, and the remaining the deg_adj_lists.
Visually
x = [atom_features, deg_slice, membership, deg_adj_list placeholders...]
| Parameters: |
|
|---|---|
| Returns: | Of shape (n_atoms, n_feat), where n_feat is number of atom_features |
| Return type: | tf.Tensor |
deepchem.nn.layers.

This layer exists because the Keras LSTM layer is intrinsically linked to an RNN with sequence inputs, and here, we will not be using sequence inputs, but rather we generate a sequence of inputs using the intermediate outputs of the LSTM, and so will require step by step operation of the lstm
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.layers.
ResiLSTMEmbedding
(n_test, n_support, n_feat, max_depth, init='glorot_uniform', activation='linear', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
Embeds its inputs using an LSTM layer.
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(argument, mask=None)[source]¶Execute this layer on input tensors.
| Parameters: | argument (list) – List of two tensors (X, Xp). X should be of shape (n_test, n_feat) and Xp should be of shape (n_support, n_feat) where n_test is the size of the test set, n_support that of the support set, and n_feat is the number of per-atom features. |
|---|---|
| Returns: | Returns two tensors of same shape as input. Namely the output shape will be [(n_test, n_feat), (n_support, n_feat)] |
| Return type: | list |
deepchem.nn.layers.
graph_conv
(atoms, deg_adj_lists, deg_slice, max_deg, min_deg, W_list, b_list)[source]¶Core tensorflow function implementing graph convolution
| Parameters: |
|
|---|---|
| Returns: | Of shape (n_atoms, n_feat) |
| Return type: | tf.Tensor |
deepchem.nn.layers.
graph_gather
(atoms, membership_placeholder, batch_size)[source]¶| Parameters: |
|
|---|---|
| Returns: | Of shape (batch_size, n_feat) |
| Return type: | tf.Tensor |
deepchem.nn.layers.
graph_pool
(atoms, deg_adj_lists, deg_slice, max_deg, min_deg)[source]¶| Parameters: |
|
|---|---|
| Returns: | Of shape (batch_size, n_feat) |
| Return type: | tf.Tensor |
Ops for graph construction.
Large amounts of code borrowed from Keras. Will try to incorporate into DeepChem properly.
deepchem.nn.model_ops.
add_bias
(tensor, init=None, name=None)[source]¶Add a bias term to a tensor.
| Parameters: | |
|---|---|
| Returns: | A biased tensor with the same shape as the input tensor. |
| Return type: | tf.Tensor |
deepchem.nn.model_ops.
binary_crossentropy
(output, target, from_logits=False)[source]¶Binary crossentropy between an output tensor and a target tensor.
output: A tensor. target: A tensor with the same shape as output. from_logits: Whether output is expected to be a logits tensor.
By default, we consider that output encodes a probability distribution.
deepchem.nn.model_ops.
cast_to_floatx
(x)[source]¶Cast a Numpy array to the default Keras float type.
| Parameters: | x (Numpy array.) – |
|---|---|
| Returns: | |
| Return type: | The same Numpy array, cast to its new type. |
deepchem.nn.model_ops.
categorical_crossentropy
(output, target, from_logits=False)[source]¶Categorical crossentropy between an output tensor and a target tensor, where the target is a tensor of the same shape as the output.
# TODO(rbharath): Should probably swap this over to tf mode.
deepchem.nn.model_ops.
clip
(x, min_value, max_value)[source]¶Element-wise value clipping.
| Returns: | |
|---|---|
| Return type: | A tensor. |
deepchem.nn.model_ops.
concatenate
(tensors, axis=-1)[source]¶Concatenates a list of tensors alongside the specified axis.
| Returns: | |
|---|---|
| Return type: | A tensor. |
deepchem.nn.model_ops.
cosine_distances
(test, support)[source]¶Computes pairwise cosine distances between provided tensors
| Parameters: |
|
|---|---|
| Returns: | Of shape (n_test, n_support) |
| Return type: | tf.Tensor |
deepchem.nn.model_ops.
dot
(x, y)[source]¶Multiplies 2 tensors (and/or variables) and returns a tensor. When attempting to multiply a ND tensor with a ND tensor, it reproduces the Theano behavior. (e.g. (2, 3).(4, 3, 5) = (2, 4, 5))
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor, dot product of x and y. |
deepchem.nn.model_ops.
dropout
(tensor, dropout_prob, training=True, training_only=True)[source]¶Random dropout.
This implementation supports “always-on” dropout (training_only=False), which can be used to calculate model uncertainty. See Gal and Ghahramani, http://arxiv.org/abs/1506.02142.
| Parameters: | |
|---|---|
| Returns: | A tensor with the same shape as the input tensor. |
| Return type: | tf.Tensor |
deepchem.nn.model_ops.
elu
(x, alpha=1.0)[source]¶Exponential linear unit.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor. |
deepchem.nn.model_ops.
epsilon
()[source]¶Returns the value of the fuzz factor used in numeric expressions.
| Returns: | |
|---|---|
| Return type: | A float. |
deepchem.nn.model_ops.
euclidean_distance
(test, support, max_dist_sq=20)[source]¶Computes pairwise euclidean distances between provided tensors
TODO(rbharath): BROKEN! THIS DOESN’T WORK!
| Parameters: |
|
|---|---|
| Returns: | Of shape (n_test, n_support) |
| Return type: | tf.Tensor |
deepchem.nn.model_ops.
fully_connected_layer
(tensor, size=None, weight_init=None, bias_init=None, name=None)[source]¶Fully connected layer.
| Parameters: | |
|---|---|
| Returns: | A new tensor representing the output of the fully connected layer. |
| Return type: | tf.Tensor |
| Raises: |
|
deepchem.nn.model_ops.
get_dtype
(x)[source]¶Returns the dtype of a Keras tensor or variable, as a string.
| Parameters: | x (Tensor or variable.) – |
|---|---|
| Returns: | |
| Return type: | String, dtype of x. |
deepchem.nn.model_ops.
get_ndim
(x)[source]¶Returns the number of axes in a tensor, as an integer.
| Parameters: | x (Tensor or variable.) – |
|---|---|
| Returns: | |
| Return type: | Integer (scalar), number of axes. |
deepchem.nn.model_ops.
get_uid
(prefix='')[source]¶Provides a unique UID given a string prefix.
| Parameters: | prefix (string.) – |
|---|---|
| Returns: | |
| Return type: | An integer. |
deepchem.nn.model_ops.
hard_sigmoid
(x)[source]¶Segment-wise linear approximation of sigmoid. Faster than sigmoid. Returns 0. if x < -2.5, 1. if x > 2.5. In -2.5 <= x <= 2.5, returns 0.2 * x + 0.5.
| Parameters: | x (A tensor or variable.) – |
|---|---|
| Returns: | |
| Return type: | A tensor. |
deepchem.nn.model_ops.
in_train_phase
(x, alt)[source]¶Selects x in train phase, and alt otherwise. Note that alt should have the same shape as x.
| Returns: | |
|---|---|
| Return type: | Either x or alt based on K.learning_phase. |
deepchem.nn.model_ops.
int_shape
(x)[source]¶Returns the shape of a Keras tensor or a Keras variable as a tuple of integers or None entries.
| Parameters: | x (Tensor or variable.) – |
|---|---|
| Returns: | |
| Return type: | A tuple of integers (or None entries). |
deepchem.nn.model_ops.
l2_normalize
(x, axis)[source]¶Normalizes a tensor wrt the L2 norm alongside the specified axis.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor. |
deepchem.nn.model_ops.
learning_phase
()[source]¶Returns the learning phase flag.
The learning phase flag is a bool tensor (0 = test, 1 = train) to be passed as input to any Keras function that uses a different behavior at train time and test time.
deepchem.nn.model_ops.
logits
(features, num_classes=2, weight_init=None, bias_init=None, dropout_prob=None, name=None)[source]¶Create a logits tensor for a single classification task.
You almost certainly don’t want dropout on there – it’s like randomly setting the (unscaled) probability of a target class to 0.5.
| Parameters: |
|
|---|---|
| Returns: | A logits tensor with shape batch_size x num_classes. |
deepchem.nn.model_ops.
lrelu
(alpha=0.01)[source]¶Create a leaky rectified linear unit function.
This function returns a new function that implements the LReLU with a specified alpha. The returned value can be used as an activation function in network layers.
| Parameters: | alpha (float) – the slope of the function when x<0 |
|---|---|
| Returns: | |
| Return type: | a function f(x) that returns alpha*x when x<0, and x when x>0. |
deepchem.nn.model_ops.
max
(x, axis=None, keepdims=False)[source]¶Maximum value in a tensor.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor with maximum values of x. |
deepchem.nn.model_ops.
mean
(x, axis=None, keepdims=False)[source]¶Mean of a tensor, alongside the specified axis.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor with the mean of elements of x. |
deepchem.nn.model_ops.
multitask_logits
(features, num_tasks, num_classes=2, weight_init=None, bias_init=None, dropout_prob=None, name=None)[source]¶Create a logit tensor for each classification task.
| Parameters: |
|
|---|---|
| Returns: | A list of logit tensors; one for each classification task. |
deepchem.nn.model_ops.
normalize_batch_in_training
(x, gamma, beta, reduction_axes, epsilon=0.001)[source]¶Computes mean and std for batch then apply batch_normalization on batch.
| Returns: | |
|---|---|
| Return type: | A tuple length of 3, (normalized_tensor, mean, variance). |
deepchem.nn.model_ops.
ones
(shape, dtype=None, name=None)[source]¶Instantiates an all-ones tensor variable and returns it.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A Keras variable, filled with 1.0. |
deepchem.nn.model_ops.
optimizer
(optimizer='adam', learning_rate=0.001, momentum=0.9)[source]¶Create model optimizer.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A training Optimizer. |
| Raises: |
|
deepchem.nn.model_ops.
random_normal_variable
(shape, mean, scale, dtype=tf.float32, name=None, seed=None)[source]¶Instantiates an Keras variable filled with samples drawn from a normal distribution and returns it.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tf.Variable, filled with drawn samples. |
deepchem.nn.model_ops.
random_uniform_variable
(shape, low, high, dtype=tf.float32, name=None, seed=None)[source]¶Instantiates an variable filled with samples drawn from a uniform distribution and returns it.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tf.Variable, filled with drawn samples. |
deepchem.nn.model_ops.
relu
(x, alpha=0.0, max_value=None)[source]¶Rectified linear unit. With default values, it returns element-wise max(x, 0).
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor. |
deepchem.nn.model_ops.
selu
(x)[source]¶Scaled Exponential Linear unit.
| Parameters: | x (A tensor or variable.) – |
|---|---|
| Returns: | |
| Return type: | A tensor. |
References
deepchem.nn.model_ops.
softmax_N
(tensor, name=None)[source]¶Apply softmax across last dimension of a tensor.
| Parameters: |
|
|---|---|
| Returns: | A tensor with softmax-normalized values on the last dimension. |
deepchem.nn.model_ops.
sparse_categorical_crossentropy
(output, target, from_logits=False)[source]¶Categorical crossentropy between an output tensor and a target tensor, where the target is an integer tensor.
deepchem.nn.model_ops.
sqrt
(x)[source]¶Element-wise square root.
| Parameters: | x (input tensor.) – |
|---|---|
| Returns: | |
| Return type: | A tensor. |
deepchem.nn.model_ops.
sum
(x, axis=None, keepdims=False)[source]¶Sum of the values in a tensor, alongside the specified axis.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor with sum of x. |
deepchem.nn.model_ops.
switch
(condition, then_expression, else_expression)[source]¶Switches between two operations depending on a scalar value (int or bool). Note that both then_expression and else_expression should be symbolic tensors of the same shape.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | The selected tensor. |
deepchem.nn.model_ops.
var
(x, axis=None, keepdims=False)[source]¶Variance of a tensor, alongside the specified axis.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A tensor with the variance of elements of x. |
deepchem.nn.model_ops.
weight_decay
(penalty_type, penalty)[source]¶Add weight decay.
| Parameters: | model – TensorflowGraph. |
|---|---|
| Returns: | A scalar tensor containing the weight decay cost. |
| Raises: | NotImplementedError –
If an unsupported penalty type is requested. |
deepchem.nn.model_ops.
zeros
(shape, dtype=tf.float32, name=None)[source]¶Instantiates an all-zeros variable and returns it.
| Parameters: |
|
|---|---|
| Returns: | |
| Return type: | A variable (including Keras metadata), filled with 0.0. |
Ops for objectives
Code borrowed from Keras.
deepchem.nn.objectives.
KLD
(y_true, y_pred)¶deepchem.nn.objectives.
MAE
(y_true, y_pred)¶deepchem.nn.objectives.
MAPE
(y_true, y_pred)¶deepchem.nn.objectives.
MSE
(y_true, y_pred)¶deepchem.nn.objectives.
MSLE
(y_true, y_pred)¶deepchem.nn.objectives.
cosine
(y_true, y_pred)¶deepchem.nn.objectives.
kld
(y_true, y_pred)¶deepchem.nn.objectives.
mae
(y_true, y_pred)¶deepchem.nn.objectives.
mape
(y_true, y_pred)¶deepchem.nn.objectives.
mse
(y_true, y_pred)¶deepchem.nn.objectives.
msle
(y_true, y_pred)¶Ops for regularizers
Code borrowed from Keras.
deepchem.nn.regularizers.
ActivityRegularizer
¶alias of L1L2Regularizer
deepchem.nn.regularizers.
L1L2Regularizer
(l1=0.0, l2=0.0)[source]¶Bases: deepchem.nn.regularizers.Regularizer
Regularizer for L1 and L2 regularization.
deepchem.nn.regularizers.
WeightRegularizer
¶alias of L1L2Regularizer
Created on Thu Mar 30 14:02:04 2017
@author: michael
deepchem.nn.weave_layers.
AlternateWeaveGather
(batch_size, n_input=128, gaussian_expand=False, init='glorot_uniform', activation='tanh', epsilon=0.001, momentum=0.99, **kwargs)[source]¶Bases: deepchem.nn.weave_layers.WeaveGather
Alternate implementation of weave gather layer corresponding to AlternateWeaveLayer
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
build
()¶call
(x, mask=None)[source]¶Execute this layer on input tensors.
x = [atom_features, atom_split]
| Parameters: |
|
|---|---|
| Returns: | outputs – Tensor of molecular features |
| Return type: | Tensor |
gaussian_histogram
(x)¶deepchem.nn.weave_layers.
AlternateWeaveLayer
(max_atoms, n_atom_input_feat=75, n_pair_input_feat=14, n_atom_output_feat=50, n_pair_output_feat=50, n_hidden_AA=50, n_hidden_PA=50, n_hidden_AP=50, n_hidden_PP=50, update_pair=True, init='glorot_uniform', activation='relu', dropout=None, **kwargs)[source]¶Bases: deepchem.nn.weave_layers.WeaveLayer
Alternate implementation of weave module same variables, different graph structures
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
build
()¶“Construct internal trainable weights.
call
(x, mask=None)[source]¶Execute this layer on input tensors.
x = [atom_features, pair_features, pair_split, atom_split, atom_to_pair]
| Parameters: |
|
|---|---|
| Returns: |
|
deepchem.nn.weave_layers.
WeaveConcat
(batch_size, n_atom_input_feat=50, n_output=128, init='glorot_uniform', activation='tanh', **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
” Concat a batch of molecules into a batch of atoms
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.weave_layers.
WeaveGather
(batch_size, n_input=128, gaussian_expand=False, init='glorot_uniform', activation='tanh', epsilon=0.001, momentum=0.99, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
” Gather layer of Weave model a batch of normalized atom features go through a hidden layer, then summed to form molecular features
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
deepchem.nn.weave_layers.
WeaveLayer
(max_atoms, n_atom_input_feat=75, n_pair_input_feat=14, n_atom_output_feat=50, n_pair_output_feat=50, n_hidden_AA=50, n_hidden_PA=50, n_hidden_AP=50, n_hidden_PP=50, update_pair=True, init='glorot_uniform', activation='relu', dropout=None, **kwargs)[source]¶Bases: deepchem.nn.copy.Layer
” Main layer of Weave model For each molecule, atom features and pair features are recombined to generate new atom(pair) features
Detailed structure and explanations: https://arxiv.org/abs/1603.00856
__call__
(x)¶Wrapper around self.call(), for handling :param x: :type x: Can be a tensor or list/tuple of tensors.
add_loss
(losses, inputs=None)¶Adds losses to model.
add_weight
(shape, initializer, regularizer=None, name=None)¶Adds a weight variable to the layer.
| Parameters: |
|
|---|
call
(x, mask=None)[source]¶Execute this layer on input tensors.
x = [atom_features, pair_features, atom_mask, pair_mask]
| Parameters: |
|
|---|---|
| Returns: |
|
Imports a number of useful deep learning primitives into one place.


A fitbit is a smartwatch tshat tracks the persons step, heart rate, miles walked, and calories burned. The fitbit helps people to stay healthy or become more healthier and to stay motivated to move around.
When you buy the fitbit in the box it tells you to download their app to see your progress throughout the days and weeks. In the app it also has leaderboard where you can compete with your friends who have fitbits. According to Business Insider, “There's tons of stuff within the application, but one of the most interesting aspects is the ability to add friends who also have a Fitbit and compete with them for steps on a daily basis.” What attracts people that have fitbit to keep themselves motivated is to beat their friends on steps they take.
In the fitbit app it shows the person the exercises that they have done throughout the week. According to Business Insider, “Each of my actions came to be less about doing something I enjoyed — from enjoying the crunchy, sweet deliciousness of a mid-afternoon snack to sweating it out at a candlelit yoga class — and more about how it would weigh into a bigger, calculated view of my overall ‘fitness’.” It also let the user know the time they did Yoga and how much calories they burn. This would help people who want to lose weight and keep track of how much they have lost weight.
In the fitbit for 8 or 9 hours in every hour it reminds you to walk so that you can reach 250 steps or more. According to fitbit blog, “The reminders nudge you to walk at least 250 steps each hour. You'll feel a vibration and see a reminder on your screen at 10 minutes before the hour if you haven't walked 250 steps.” This shows that it makes sure that the user keeps on moving to gain more steps throughout the day.


Scope
{{ latest_landing.scope | markdownify }}
{{ latest_landing.background | markdownify }}
{{ latest_landing.scope | markdownify }}
Participants are responsible for their own transportation and accommodation. Registration to the event is free but participants are expected to cover travel, accommodation and meals during the time of the meeting. The venue (Chateauform Les Berges de Seine) of this event will be informed of your participation as soon as you complete the online registration. Participants will be contacted by the venue to complete their hotel reservation at this venue.
There will be a minimum of fifteen travel rewards for the invited applicants. The travel rewards will be offered on a case-by-case basis based on the application’s relevance to ELIXIR’s technical activities and strategy.
29 hacking projects have been preselected aiming to prepare well in advance productive hacking sessions. Please feel free to browse the projects and contact the representatives if you want to contribute or find out more about any of the hacking projects.
Though the call for hacking project proposals is close and preselected hacking projects will have priority, the biohackathon welcomes new ideas and it is open to more project proposals. If you would still like to propose a project please contact<EMAIL_ADDRESS>The first call for proposals was closed on May 23 and 18 proposals were accepted. The second call for proposal was closed in July 12 and 11 proposal were accepted.
We aim to support and promote accepted proposals but we welcome new ideas.
A full list for the selected hacking projects can be found on the following link
Hacking projectsThe BioHackathon Schedule can be found here
ProgrammeThe capacity of the event is full. The registration is closed. If you want to be on reserve list, please contact dana.cernoskova@elixir-europe.org.


Modules | |
| TIMER_MGR Data Structures | |
| TIMER_MGR Functions | |
| TIMER_MGR Enumerated Data Types | |
===========================================================================
This is the CSL-FL API documentation for the timer_mgr module.
The following sequence of API calls must be followed to properly configure and initialize the timer_mgr.
Enable the desired timers. There are two ways to do this: a) To enable all timers, call the CSL_timermgrEnableAllTimers function. This will enable all timers from 0 .. CSL_timermgrGetMaxTimers. b) Enable (or disable) individual timers by calling the CSL_timermgrSetTimerEnable function for each timer.
If less than half of the available timers are to be enabled initially, it is faster to enable individual timers by calling CSL_timermgrSetTimerEnable with bEnable=true. If more than half of the available timers are to be enabled initially, it is faster to call CSL_timermgrEnableAllTimers and then disable specific timers by calling CSL_timermgrSetTimerEnable with bEnable=false.
Once the timer_mgr has been configured and enabled, the following functions can be called to service the active timers: CSL_timermgrIsTimerExpired CSL_timermgrGetExpiredTimers CSL_timermgrTouchTimer


Fisher Spanish - Speech, Linguistic Data Consortium (LDC) catalog number LDC2010T04 and isbn 1-58563-536-7, was developed by LDC and consists of audio files covering roughly 163 hours of telephone speech from 136 native Caribbean Spanish and non-Caribbean Spanish speakers. Full orthographic transcripts of these audio files are available in Fisher Spanish - Transcripts (LDC2010T04).
The Fisher telephone conversation collection protocol was created at LDC to address a critical need of developers trying to build robust automatic speech recognition (ASR) systems. Previous collection protocols, such as CALLFRIEND and Switchboard-II and the resulting corpora, have been adapted for ASR research but were in fact developed for language and speaker identification respectively. Although the CALLHOME protocol and corpora were developed to support ASR technology, they feature small numbers of speakers making telephone calls of relatively long duration with narrow vocabulary across the collection. CALLHOME conversations are challengingly natural and intimate. Under the Fisher protocol, a very large number of participants each make a few calls of short duration speaking to other participants, whom they typically do not know, about assigned topics. This maximizes inter-speaker variation and vocabulary breadth although it also increases formality.
Previous protocols such as CALLHOME, CALLFRIEND and Switchboard relied upon participant activity to drive the collection. Fisher is unique in being platform driven rather than participant driven. Participants who wish to initiate a call may do so; however the collection platform initiates the majority of calls. Participants need only answer their phones at the times they specified when registering for the study.
To encourage a broad range of vocabulary, Fisher participants are asked to speak on an assigned topic which is selected at random from a list, which changes every 24 hours and which is assigned to all subjects paired on that day. Some topics are inherited or refined from previous Switchboard studies while others were developed specifically for the Fisher protocol.
In collecting data for this corpus, attempts were made to provide a representative distribution of subjects across a variety of demographic categories including: gender, age, dialect region, and education level.
This corpus joins other Fisher corpora: Arabic CTS Levantine Fisher Training Data Set 3 (LDC2005S07, LDC2005T03), Fisher English Training Part 2 (LDC2005S13, LDC2005T19), Fisher English Training Speech Part 1 (LDC2004S13, LDC2004T19), and Fisher Levantine Arabic Conversational Telephone Speech (LDC2007S02, LDC2007T04)
The speech recordings consist of 819 telephone conversations of 10 to 12 minutes in duration. They are provided as digital audio files in NIST SPHERE format (1024-byte ASCII file headers). The conversations were recorded as 2-channel mu-law sample data with 8000 samples per second (as captured from the public telephone network).
The accompanying transcript files (availabel in Fisher Spanish - Transcripts (LDC2010T04)) are in plain-text, tab-delimited format (tdf) with UTF-8 character encoding. They were created with the LDC-developed transcription tool "XTrans", which allowed for improved handling of multi-channel audio and overlapping speakers. XTrans is available from LDC.
Transcribers followed LDC's Transcription Guidelines (NQTR), which are included with the documentation for this release.
The first line of each transcript file provides the column headings; the next two lines are "comments" that can be ignored (these are used by XTrans; they are distinguished from non-comment lines by having an initial semicolon ";"). Actual transcript data, with time stamps, channel number, transcript text and additional information, begins at line 4 of each transcript file.
Native speakers of Caribbean Spanish and non-Caribbean Spanish were
recruited from within the continental United States and Puerto
Rico. The following tables provide an overview of the demographics
of the participants. The Subjects Table file, provided in the
documentation, may be used to answer questions about specific
combinations of participant characteristics (including level of
participation).
|
|
|
|
|
|
Please see file_d1.tbl and file_d2.tbl for the directory structure of this publication, as well as a complete list of files.
Go to data for data files.
Other documentation files are:
Additional information, updates, bug fixes may be available in the LDC catalog entry for this corpus at LDC2010S01.
Portions © 2005-2006, 2010 Trustees of the University of Pennsylvania


#include <ReceiverPdu.h>
Public Member Functions | |
| ReceiverPdu () | |
| virtual | ~ReceiverPdu () |
| virtual void | marshal (DataStream &dataStream) const |
| virtual void | unmarshal (DataStream &dataStream) |
| unsigned short | getReceiverState () const |
| void | setReceiverState (unsigned short pX) |
| unsigned short | getPadding1 () const |
| void | setPadding1 (unsigned short pX) |
| float | getReceivedPoser () const |
| void | setReceivedPoser (float pX) |
| EntityID & | getTransmitterEntityId () |
| const EntityID & | getTransmitterEntityId () const |
| void | setTransmitterEntityId (const EntityID &pX) |
| unsigned short | getTransmitterRadioId () const |
| void | setTransmitterRadioId (unsigned short pX) |
| virtual int | getMarshalledSize () const |
| bool | operator== (const ReceiverPdu &rhs) const |
Protected Attributes | |
| unsigned short | _receiverState |
| unsigned short | _padding1 |
| float | _receivedPoser |
| EntityID | _transmitterEntityId |
| unsigned short | _transmitterRadioId |
Copyright (c) 2007-2009, MOVES Institute, Naval Postgraduate School. All rights reserved.
| ReceiverPdu::ReceiverPdu | ( | ) |
| ReceiverPdu::~ReceiverPdu | ( | ) | [virtual] |
| int ReceiverPdu::getMarshalledSize | ( | ) | const [virtual] |
Reimplemented from DIS::RadioCommunicationsFamilyPdu.
| unsigned short ReceiverPdu::getPadding1 | ( | ) | const |
| float ReceiverPdu::getReceivedPoser | ( | ) | const |
| unsigned short ReceiverPdu::getReceiverState | ( | ) | const |
| const EntityID & ReceiverPdu::getTransmitterEntityId | ( | ) | const |
| EntityID & ReceiverPdu::getTransmitterEntityId | ( | ) |
| unsigned short ReceiverPdu::getTransmitterRadioId | ( | ) | const |
| void ReceiverPdu::marshal | ( | DataStream & | dataStream | ) | const [virtual] |
Reimplemented from DIS::RadioCommunicationsFamilyPdu.
| bool ReceiverPdu::operator== | ( | const ReceiverPdu & | rhs | ) | const |
| void ReceiverPdu::setPadding1 | ( | unsigned short | pX | ) |
| void ReceiverPdu::setReceivedPoser | ( | float | pX | ) |
| void ReceiverPdu::setReceiverState | ( | unsigned short | pX | ) |
| void ReceiverPdu::setTransmitterEntityId | ( | const EntityID & | pX | ) |
| void ReceiverPdu::setTransmitterRadioId | ( | unsigned short | pX | ) |
| void ReceiverPdu::unmarshal | ( | DataStream & | dataStream | ) | [virtual] |
Reimplemented from DIS::RadioCommunicationsFamilyPdu.
unsigned short DIS::ReceiverPdu::_padding1 [protected] |
padding
float DIS::ReceiverPdu::_receivedPoser [protected] |
received power
unsigned short DIS::ReceiverPdu::_receiverState [protected] |
encoding scheme used, and enumeration
EntityID DIS::ReceiverPdu::_transmitterEntityId [protected] |
ID of transmitter
unsigned short DIS::ReceiverPdu::_transmitterRadioId [protected] |
ID of transmitting radio


Niagara Niagara (R) *** (out of ****) Bob Gosse's Niagara Niagara follows a blueprint not unlike a lot of young-lovers-on-the-road movies. Wild Marcy (Robin Tunney) and calm Seth (Henry Thomas) meet cute, literally running into each other while shoplifting at a local store. A mere couple of scenes later, the two embark on a journey to Toronto from their small, unnamed American town in pursuit of a rare doll that Marcy desperately wants. Along the way, true love inevitably blossoms. What sets Niagara Niagara apart, though, is that Marcy is afflicted with Tourette's Syndrome, a neurological disorder that causes sudden muscle and vocal tics. Tunney, displaying an acting range not hinted at in the teenage witch thriller The Craft, delivers an astonishing performance that won her the Best Actress prize at last year's Venice Film Festival. To term her work a tour-de-force is not to imply that she attacks the scenery; Tunney's effectiveness lies in her modulation and vulnerability, which makes her depiction of Marcy's illness--which often causes her to act violently--that much more convincing and tragic. She and the nicely subtle Thomas develop a sweetly innocent and beguilingly off-kilter chemistry. Their journey hits a few rough spots creatively along the way, mostly the fault of writer Matthew Weiss. A detour involving a kindly widower (Michael Parks) who takes the couple in brings the story to a screeching halt, and the key character of a trigger-happy pharmacist (Stephen Lang) is highly unbelievable. But these missteps do not blunt the power of Tunney's bravura turn, which carries Niagara Niagara to a level of poignance it would not have otherwise achieved. (opens March 20)
Michael Dequina<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS><EMAIL_ADDRESS>|<EMAIL_ADDRESS><EMAIL_ADDRESS>|<EMAIL_ADDRESS>Mr. Brown's Movie Site: http://welcome.to/mrbrown Personal Page: http://welcome.to/w3md CinemaReview Magazine: http://www.CinemaReview.com
Michael Jordan Beyond the Court: http://fly.to/michaeljordan A Michael Jordan Fan's Heartbreak: http://fly.to/mj23
"I didn't know what to expect. It's like something you chase for so long, but then you don't know how to react when you get it. I still don't know how to react." --Michael Jordan, on winning his first NBA championship in 1991 ...or, my thoughts after meeting him on November 21, 1997
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Pair programming is a software development technique wherein two programmers work together to produce code. There are two roles in pair programming - the driver and the navigator. The driver writes the code while the navigator directs the code being written and reviews each line of code as it is written. It is typical that the driver and the navigator switch roles frequently - even within one session.
Pair programming is important because it improves design quality and makes problem solving easier. Two heads are better than one. Pair programming also facilitates learning and sharing knowledge, whether it be tips regarding programming languages or overall design skill. Pair programming is also important because it facilitates team building and communication amongst programmers.
Pair programming is important at DBC for the reasons stated above, and because it prepares the boots for positions they will obtain when they enter the workforce as programmers.
This informational website was created by Amber Hager and Lorena Mesa.
I did not have a pair partner, so I worked with the instructor. I enjoyed pair programming, particularly because Lorena gave me some helpful tips and links about certain programming aspects that I did not know before. We did as much as we could in the hour and then I was left to complete the challenge on my own, which involved CSS formatting, a topic I'm still a little shaky on. I'd say everything worked except that I'm not happy with the placement of the navigation bar (we used a different approach than I'm used to and I can't get it to center).


API Metrics
Now that this API is published to the gateway, it may be consumed by
API clients. Use this tab to view basic metrics/analytics for the API.
Show
metrics data for the
Overall Usage
No data found.
Top Client Usage
No data found.
Usage by Plan
No data found.
Successful Responses
No data found.
Failed Responses
No data found.
Error Responses
No data found.


--- title: Pupil rev 0.3.5 Release description: > Pupil Labs software release created: !!timestamp '2013-11-14 11:00:00' author: Moritz & Will -- Pupil Team --- {% mark excerpt -%} {%- endmark %} {% mark body -%} Another quick release of our recent development work on Pupil. Updates are recommended!
## Improved Pupil Detection The new Pupil detector is more sophisticated and should preform much better. The False Positive rate (ghost pupils) has been dramatically reduced. We have used a load of eye recordings and can say that the new detector kill's our old version :-). Of course there is still lots left to improve and we have ideas to push pupil detection to new levels soon. But for now, here the release notes: * Pupil detection now pre-filters contours based on curvature properties. * Ellipse fitting now utilizes fitting error to discard bad ellipses * Our detector now matches and combines contour segments to find a maximally supported ellipse. * The result is now augmented with a confidence measure (from 0 to 1). * New debug window for everybody that likes visual feedback from the pupil detector Please have a look at the following parameters for the pupil detector: * pupil_intensity_range determines the degree of darkness required for edges to be considered (have a look at the eye image with algorithm view active) * pupil_min & pupil_max the range of admissible pupil sizes, set accordingly. ## Improved Point Display GL points now have custom shaders for improved display. This means that Linux users that experienced square points should now see proper circle points. ## Proper Logging Pupil now uses a proper logging system and stream information into logfiles in the user_settings dir. This should simplify diagnostics and debugging ## Getting the Update If you want to just use Pupil Software and don't need to make changes to the source code, you can download and run our latest standalone Pupil app bundles from our Github release page. ## Build dependencies If you want to install dependencies yourself (this means not using the bundle packages), please note the followingchanges/additions: ### Mac
pip install numexpr
### Linux
Install new version on numpy, numexpr, scipy
sudo pip install numpy
sudo pip install scipy
sudo pip install numexpr
sudo pip install matplotlib
Install OpenCV <IP_ADDRESS> and dependencies via script
curl https://raw.github.com/jayrambhia/Install-OpenCV/master/Ubuntu/2.4/opencv2_4_6_1.sh | sh
Please let us know what you think and let us know if you find any bugs via github issues or on the forum!
## Next steps
We have not yet implemented screen tracking because we decided to first do a few things we think are important improvements (see above). But dont worry, a large part of the next release will be dedicated to screen tracking!
{%- endmark %}


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
VoiceMailTel Inc.
Legal Name:
VoiceMailTel Inc.
Operating Name:
VoiceMailTel
Mailing Address:
308-6045 Creditview RdMISSISSAUGA, Ontario
L5V 0B1
Location Address:
700-2 Bloor St WTORONTO, Ontario
M4W 3R1
Tel.:<PHONE_NUMBER>
Tel.: (866) 6VO-ICEM
Fax:<PHONE_NUMBER>
Logo
Company Profile
VoiceMailTel Virtual Phone Service is the most comprehensive and affordable communication service available for small and home-based businesses and mobile workers. Using our solution your business will grow faster and its image will be improved. Give your business a better chance, our Virtual Office is best for you!
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
Market profile
Industry sector market interests:
- Agriculture
- Construction
- Consumer Products
- Culture
- Environment
- Fishery
- Forestry
- Information Technology and Telecommunications
- Manufacturing
- Medical/Biotechnology/Chemical
- Mining/Petroleum/Gas
- Service Industry
- Tourism
- Transportation
- Wholesale/Retail
- Aerospace
- Defence
- Automotive
- Food and Beverage Manufacturing
- Furniture and Wood Product
- Pulp and Paper
- Plastics and Rubber Products
- Primary and Fabricated Metal
- Electrical Equipment
- Textile and Clothing
Sector information
Key / Major clients:
Firms Hosting Inc.Central Organization Inc.
FiatLux Solution
The Bans Consulting Group
Best Homes Chicago
Jordan's HCA
Testimonial:
VoiceMailTel phone service has worked great for us! We can even have our receptionist answer at the office and transfer the calls to us in the field - as if we were right down the hall! We have received so many compliments on the auto attendant feature - we have even referred 4 customers! Thanks, VoiceMailTel!Anthony J., Firms Hosting Inc.
--------------------------------------------------------------------------------
We switched to VoiceMailTel from [another provider], and for all of the right reasons. Great customer service, awesome features, and no hidden fees. Keep up the great work!
Lumi Bica, Central Organization Inc.
--------------------------------------------------------------------------------
VoiceMailTel helped us give a great impression to our customers - instead of having an unprofessional looking cell phone number on our homepage we can have a virtual PBX system, with both local and toll free number. The call forward feature is great for us, since a lot of our business is done from home or on the road. Overall, it's a great product with a ton of awesome features that has significantly improved the way we do business.
John B., FiatLux Solution
--------------------------------------------------------------------------------
VoiceMailTel has provided us a great routing and call forwarding communication solution for a consulting business where we have offices and projects in different locations.
A.M. Dobrin, The Bans Consulting Group
--------------------------------------------------------------------------------
Greetings! My name is Maria Grand, I handle PR in a small Real Estate business that started working with your system not long ago. I am writing you to express our gratitude and satisfaction towards your services. When at first I presented your offer to my boss, he was little confident, but reviewing your rates he gave me permission to use your 30 Days Free Trial. We have few employees in the company, but all of us are constantly on the move and the communication within the company or even with the costumers was a bit scarce and chaotic. Now that we have your virtual phone service we can maintain a perfect contact between each other and with our customers and partners, too. Costs have decreased and our company is evolving much faster. Our partners said they will choose your service as well. So thank you for the services you provided us!
Maria Grand, Best Homes Chicago
--------------------------------------------------------------------------------
We are a small healthcare company taking care of the people from our area. As a good communication between us and the patients is vital, VoiceMailTel helped us a lot. We not only pay much less than we used to, but we have a Virtual Office that permits us to be mobile and still take any calls. It's a great service, we are very satisfied.
Roger Jordan, Jordan's HCA
--------------------------------------------------------------------------------
When we started our company we thought succeeding in the field of publishing and distributing Mathematics' merchandising was almost an impossible aim with all the costs involved and above all, with such a strong competition. We are 3 partners, always on the move, always in hurry and always trying to find new ways of advertising our company. We found out about VoiceMailTel's service and we thought we should give it a try. I can tell you now we have a great image, like the ones big companies have, that instantly increased our sales and our clients' trust in us. We have a greater liberty of move now as our clients can reach us anywhere and anytime as we enabled our calls to be redirected on our mobile phones and as we have virtual extensions, our clients can reach any of us, without waiting anymore. A great improvement for our company: a cost-effective solution that gave us a break in the business.
John Bradley, Mathematics Toronto Ltd.
--------------------------------------------------------------------------------
Being a small business dealing with large clients, VoiceMailTel offered us the possibility to "stay up" with the competition and now our company is at the same level of communication with the big companies. And the best thing is that we are paying a small fraction from what they are paying for the same services. We declare ourselves a satisfied VoiceMailTel client, we will recommend your service to our friends and partners, for sure.
Jerry Brown, Woods Inc.
--------------------------------------------------------------------------------
I am a young floral shop-owner, and client of VoiceMailTel. Your service helped me out a lot; now in stead of unprofessionally listing my cell phone number on my website, I have a Toll Free which increases my chances of getting new clients. One of my friends was amazed by it, he didn't believe I was the man behind this business, and when I told him about the costs, he was simply shocked. I am paying a small fraction of what big companies are and still have an appearance like theirs. Thanks, VoiceMailTel.
Mario Green, Rosen Floral Shop
--------------------------------------------------------------------------------
My name is Albert Robinson. Recently I've started my own floral small home-based business. At start I must be very careful with my expenses so I had to search for a more cost-convenient communication method, so I found out about your service. The features that convinced me were the no hardware/software installation and the big business image given by your service. Now I have a great image in front of my customers, inspiring professionalism and trust in my company, and all this at low costs, which is making my business grow faster and faster.
Albert Robinson, Albert's Flowers
--------------------------------------------------------------------------------
VoiceMailTel is a NOT TO MISS service! I have tried several services before but either the quality wasn't reliable, or the rates were literally closing down my small business. I found out about VoiceMailTel from a fellow notary who's working with your service. I called him and I was welcomed by such a professional greeting that I thought he expanded his office overnight. To tell you the truth the customized greeting almost convinced me to chose his services, haha, but in stead I chose VoiceMailTel's and now I am so satisfied I did it that the smallest thing to do in exchange was to write this testimonial. Now I have such a professional image that clients are more and more every day and the best thing is that me and my 3 employees can handle all calls thanks to the Virtual Extensions, and the best thing is now I can give my callers more information about my services without loosing any time and without they spending anything on the call. Thanks, VoiceMailTel!
Donald Clarkson, Clarkson's Chancery
--------------------------------------------------------------------------------
As a manufacturing company, our products' number is always increasing and we try to have special offers all the time, but until now advertising them was a huge financial effort from our side. With VoiceMailTel's service now we have a targeted advertising campaign directly on our phone system and the best thing about it it's that costs have incredibly diminished, so the company is growing faster and sales are better and better.
Gary Prest, Vista Services
--------------------------------------------------------------------------------
We are a home-based casting company providing actors for movies, TV shows and commercials. People do usually call before applying their resumes, so the number of calls exceeds our possibilities to answer. We started working with VoiceMailTel not long time ago, but I can tell you we are very satisfied now. Not only clients and actors have a better, more powerful image of our business, but the best thing is now we can answer all calls without the need to stay near our home-phone as it was until now. Business is doing great, and we are much more productive. Thanks!
Helen Mist, Vista Castings
--------------------------------------------------------------------------------
I have a small management company taking care of several music groups. Until now I had some problems satisfying all their needs with calling everyone from organizers to producers, from possible sponsors to music magazines. The costs were enormous and I had serious doubts weather I should continue or not, but I found out about your services from a friend and gave it a try, although I was not confident enough. At first I was pleasantly surprised of your small rates, and now, after 3 months of working with your Virtual Office, I can handle it all: using my Toll Free number anyone can call me and have no costs, thanks to the Call Transfer feature I am no longer tied to the office and also the Calling Card helps me a lot to call from everywhere I am. And I should not forget about the small rates. I am glad I chose your service.
Adam Red, Adam Red Management
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
Market
Market profile
Industry sector market interests:
- Agriculture
- Construction
- Consumer Products
- Culture
- Environment
- Fishery
- Forestry
- Information Technology and Telecommunications
- Manufacturing
- Medical/Biotechnology/Chemical
- Mining/Petroleum/Gas
- Service Industry
- Tourism
- Transportation
- Wholesale/Retail
- Aerospace
- Defence
- Automotive
- Food and Beverage Manufacturing
- Furniture and Wood Product
- Pulp and Paper
- Plastics and Rubber Products
- Primary and Fabricated Metal
- Electrical Equipment
- Textile and Clothing
Sector
Sector information
Key / Major clients:
Firms Hosting Inc.Central Organization Inc.
FiatLux Solution
The Bans Consulting Group
Best Homes Chicago
Jordan's HCA
Testimonial:
VoiceMailTel phone service has worked great for us! We can even have our receptionist answer at the office and transfer the calls to us in the field - as if we were right down the hall! We have received so many compliments on the auto attendant feature - we have even referred 4 customers! Thanks, VoiceMailTel!Anthony J., Firms Hosting Inc.
--------------------------------------------------------------------------------
We switched to VoiceMailTel from [another provider], and for all of the right reasons. Great customer service, awesome features, and no hidden fees. Keep up the great work!
Lumi Bica, Central Organization Inc.
--------------------------------------------------------------------------------
VoiceMailTel helped us give a great impression to our customers - instead of having an unprofessional looking cell phone number on our homepage we can have a virtual PBX system, with both local and toll free number. The call forward feature is great for us, since a lot of our business is done from home or on the road. Overall, it's a great product with a ton of awesome features that has significantly improved the way we do business.
John B., FiatLux Solution
--------------------------------------------------------------------------------
VoiceMailTel has provided us a great routing and call forwarding communication solution for a consulting business where we have offices and projects in different locations.
A.M. Dobrin, The Bans Consulting Group
--------------------------------------------------------------------------------
Greetings! My name is Maria Grand, I handle PR in a small Real Estate business that started working with your system not long ago. I am writing you to express our gratitude and satisfaction towards your services. When at first I presented your offer to my boss, he was little confident, but reviewing your rates he gave me permission to use your 30 Days Free Trial. We have few employees in the company, but all of us are constantly on the move and the communication within the company or even with the costumers was a bit scarce and chaotic. Now that we have your virtual phone service we can maintain a perfect contact between each other and with our customers and partners, too. Costs have decreased and our company is evolving much faster. Our partners said they will choose your service as well. So thank you for the services you provided us!
Maria Grand, Best Homes Chicago
--------------------------------------------------------------------------------
We are a small healthcare company taking care of the people from our area. As a good communication between us and the patients is vital, VoiceMailTel helped us a lot. We not only pay much less than we used to, but we have a Virtual Office that permits us to be mobile and still take any calls. It's a great service, we are very satisfied.
Roger Jordan, Jordan's HCA
--------------------------------------------------------------------------------
When we started our company we thought succeeding in the field of publishing and distributing Mathematics' merchandising was almost an impossible aim with all the costs involved and above all, with such a strong competition. We are 3 partners, always on the move, always in hurry and always trying to find new ways of advertising our company. We found out about VoiceMailTel's service and we thought we should give it a try. I can tell you now we have a great image, like the ones big companies have, that instantly increased our sales and our clients' trust in us. We have a greater liberty of move now as our clients can reach us anywhere and anytime as we enabled our calls to be redirected on our mobile phones and as we have virtual extensions, our clients can reach any of us, without waiting anymore. A great improvement for our company: a cost-effective solution that gave us a break in the business.
John Bradley, Mathematics Toronto Ltd.
--------------------------------------------------------------------------------
Being a small business dealing with large clients, VoiceMailTel offered us the possibility to "stay up" with the competition and now our company is at the same level of communication with the big companies. And the best thing is that we are paying a small fraction from what they are paying for the same services. We declare ourselves a satisfied VoiceMailTel client, we will recommend your service to our friends and partners, for sure.
Jerry Brown, Woods Inc.
--------------------------------------------------------------------------------
I am a young floral shop-owner, and client of VoiceMailTel. Your service helped me out a lot; now in stead of unprofessionally listing my cell phone number on my website, I have a Toll Free which increases my chances of getting new clients. One of my friends was amazed by it, he didn't believe I was the man behind this business, and when I told him about the costs, he was simply shocked. I am paying a small fraction of what big companies are and still have an appearance like theirs. Thanks, VoiceMailTel.
Mario Green, Rosen Floral Shop
--------------------------------------------------------------------------------
My name is Albert Robinson. Recently I've started my own floral small home-based business. At start I must be very careful with my expenses so I had to search for a more cost-convenient communication method, so I found out about your service. The features that convinced me were the no hardware/software installation and the big business image given by your service. Now I have a great image in front of my customers, inspiring professionalism and trust in my company, and all this at low costs, which is making my business grow faster and faster.
Albert Robinson, Albert's Flowers
--------------------------------------------------------------------------------
VoiceMailTel is a NOT TO MISS service! I have tried several services before but either the quality wasn't reliable, or the rates were literally closing down my small business. I found out about VoiceMailTel from a fellow notary who's working with your service. I called him and I was welcomed by such a professional greeting that I thought he expanded his office overnight. To tell you the truth the customized greeting almost convinced me to chose his services, haha, but in stead I chose VoiceMailTel's and now I am so satisfied I did it that the smallest thing to do in exchange was to write this testimonial. Now I have such a professional image that clients are more and more every day and the best thing is that me and my 3 employees can handle all calls thanks to the Virtual Extensions, and the best thing is now I can give my callers more information about my services without loosing any time and without they spending anything on the call. Thanks, VoiceMailTel!
Donald Clarkson, Clarkson's Chancery
--------------------------------------------------------------------------------
As a manufacturing company, our products' number is always increasing and we try to have special offers all the time, but until now advertising them was a huge financial effort from our side. With VoiceMailTel's service now we have a targeted advertising campaign directly on our phone system and the best thing about it it's that costs have incredibly diminished, so the company is growing faster and sales are better and better.
Gary Prest, Vista Services
--------------------------------------------------------------------------------
We are a home-based casting company providing actors for movies, TV shows and commercials. People do usually call before applying their resumes, so the number of calls exceeds our possibilities to answer. We started working with VoiceMailTel not long time ago, but I can tell you we are very satisfied now. Not only clients and actors have a better, more powerful image of our business, but the best thing is now we can answer all calls without the need to stay near our home-phone as it was until now. Business is doing great, and we are much more productive. Thanks!
Helen Mist, Vista Castings
--------------------------------------------------------------------------------
I have a small management company taking care of several music groups. Until now I had some problems satisfying all their needs with calling everyone from organizers to producers, from possible sponsors to music magazines. The costs were enormous and I had serious doubts weather I should continue or not, but I found out about your services from a friend and gave it a try, although I was not confident enough. At first I was pleasantly surprised of your small rates, and now, after 3 months of working with your Virtual Office, I can handle it all: using my Toll Free number anyone can call me and have no costs, thanks to the Call Transfer feature I am no longer tied to the office and also the Calling Card helps me a lot to call from everywhere I am. And I should not forget about the small rates. I am glad I chose your service.
Adam Red, Adam Red Management


{{< layout}} {{$pageTitle}} {{#values.old-blue}} Old passport number {{/values.old-blue}} {{^values.old-blue}} Old passport details {{/values.old-blue}} {{/pageTitle}} {{$header}} {{#values.old-blue}}
For example B235014
For example 502135326
You'll need to send us your old passport so we can cancel it.
If you don't have your old passport, you need to apply in a different way.
If you don’t have your old passport, you must report it lost or stolen.


PhD Candidate in Mechanical Engineering Case Western Reserve University Cleveland, OH2008 to Present Dissertation: In progress


|
Custom Libraries for Propeller
1.5
List of custom libraries
|
Magnetic Rotary Encoder.
BME280 Digital Humidity, Pressure and Temperature sensor.
BME680 Environmental Sensor.
3-Axis Digital Compass IC. (Obsolete)
WaveShare Clock Module
DS3231 Real Time Clock
EM7180 Sensor Fusion Processor.
ESP8266 Parallax WX WiFi Interface.
Parallax Gyroscope Module.
HIH8120 Humidity and Temperature sensor.
HUB 75E Panel 64 Driver.
HX8357 320X480 display driver.
INA260 Voltage and Current Monitor.
JSON Encoding and Decoding Data.
Parallax Laser Ping Driver.
Parallax LCD Driver.
LSM9DS1 Parallax IMU Driver.
MC33926 Dual Motor Driver.
MAX7219 7 segement display driver.
Decode Mavlink Messages.
MCP3000 Analog to Digital Converter Driver.
DC Motor Driver.
MPU9250 9 Degrees of Freedom Driver.
MS5607 Barometric Pressure Sensor.
NewHaven Display Driver.
Nextion Display Driver.
SBUS Reciever Decoder.
Parallax 360 Servo Driver.
Set System Time.
SSD1306 OLED Display Driver.
SSD1331 Colored OLED Display Driver.
A4988 Stepper Motor Driver.
Timer Delay Logic.
Wind Speed Tracker.
WS2812 LED Driver.


--- layout: post title: Better placement for BlackBox Debug Bar in WordPress date: type: post published: false status: private categories: [] tags: - note to self - wordpress meta: _edit_last: '1' _syntaxhighlighter_encoded: '1' author: David Simpson excerpt_separator: ---
BlackBox Debug Bar is an excellent little addition to any WordPress plugin/theme developer's arsenal:
BlackBox is a plugin for plugin and theme developers. It collects and displays useful debug information (errors, executed queries, globals, profiler).
-- http://wordpress.org/plugins/blackbox-debug-bar/
I particularly like how it shows the number of PHP errors at the top of every page in full view.
One thing that bugs me is that it hides the header bar by default, so you can't select the menu. This is easily fixed with a small sprinkling of CSS.
Browse to Admin | Plugins | Installed Plugins and click on Edit under BlackBox.
Edit blackbox-debug-bar/public/styles.css.
Add this style to the top of the file:
html #wpwrap,
html #wpadminbar {
top: 28px;
}
This simply pushes the page down below the BlackBox Debug Bar so that you can see everything:
If you're serious about debugging WordPress, you should also check out Debug Bar and its family of dependent plugins:


Ocean paddle race series joins The Doctor
by Tyler BrownTHE iconic Doctor surf ski race will become a week-long affair with the introduction of a series of five ocean paddle races surrounding the event, creating a prize purse of $100,000.
The Doctor is a 27km paddle race from Rottnest Island to Sorrento Beach, which will be held on Saturday, November 23.
It is expected to attract 450 competitors, most competing on surf skis but there are categories for stand-up paddleboards, outrigger canoes and prone paddleboards.
Competitors are already registered from South Africa, America, England, New Zealand, Hong Kong, Singapore, Portugal as well as hundreds from interstate.
The WA Race Week new initiative will start on Saturday, November 16 with West Coast Downwind followed by the Sunset Surf Ski Series with race one on November 19 and race two on November 21.
There will also be the Dash for Cash on November 22 with a $15,000 prize before The Doctor on November 23, which has a $50,000 prize purse.
There is an additional $35,000 in bonus cash and prizes for points scored across all five events.
Race director, nine-time paddle world champion and Ocean Paddler owner Dean Gardiner said since The Doctor’s inception in 2002 there had never been a bigger field or such a big prize pool.
“We’ve made it really attractive for competitors to travel to Perth to compete by clustering five events in the space of a week and increasing the prize pool made possible thanks to the support of Shaw and Partners,” he said.
“We believe it’ll be the biggest open water surf ski race in the world by competitor numbers.
“It’s well known that Perth affords prime conditions for paddling and especially downwind racing thanks to the wind, the ‘Fremantle Doctor’, which the main race is named after.”


Pingendo
Assignment 2
Assignment 3
Assignment 4
EPIN 1020
Student: Chan Yu Sum Jenny
Assignment 2
I wrote a JavaScript program to create a puzzle, so players can input values through HTML form and display the result inside a box.
Click to access assignment 2
Assignment 3
Please refer to https://github.com/YSJennyChan/Problem-set-3
Assignment 4
Link to assignment 4
© Copyright 2018 Pingendo - All rights reserved.
Made with Pingendo Free


Try to make the page a standalone document that hits your target age group.
Start with a welcoming hook. Something to gain interest, some questions that need to be resolved -- whatever it takes
Collect the tasks together in some useful order.
Say 'Send us what you've done' in interestingly different ways at regular intervals.
Throw in some ephemera -- anything to keep it light hearted and welcoming.
End with some follow up suggestions, further reading, links. Invitations to submit.
Make a folder for the page in DropBox or Google Drive - let's call it twinkletoes.
Inside twinkletoes, collect all the assets that are going to live in the page -- images, videos, sounds, artwork, notes -- whatever reference material you need.
Edit a narrative for the page in markdown. You might start by using the online editor -- you type markdown on the left of the page and it displays the resulting html page on the right. Later on you'll find it convenient to switch to a text editor like Sublime Text
.
You can include any maths in the normal way -- y = x2.
StackEdit or Sublime will create an html page for you. Save this in twinkletoes
, view it in a browser. Then start inserting images.
It's easy enough to add them with markdown syntax e.g.
would refer to the file goose.jpg
in twinkletoes
, and give it a caption Here's a demented goose
.
Add in videos, sounds, artwork. Worry about layout, choice of font, colour, background graphics, animations, interactives, different screen widths, etc. etc.


STOP WASTING YOUR TIME
With the wrong # on instagram
Discipline with website blocker is doing what needs to be done
even if you don't want to do it
Block all your distractions and work with focus on your goal
Download Now
Block all your distractions and work with focus on your goal
More Than Just a Website Blocker
- Let the maximum number of minutes per day that you will be allowed to browse sites list
- Let the time at which websites blocker will reset your allowed time per day
- Add sites to get blocked
Want some suggestions of sites you would block?
Check out this list
Want to stop yourself from disabling website blocked ?
Block the chrome extensions page


| hms-package {hms} | R Documentation |
Implements an S3 class for storing and formatting time-of-day values, based on the 'difftime' class.
Maintainer: Kirill Müller<EMAIL_ADDRESS>Other contributors:
R Consortium [funder]
RStudio [funder]
Useful links:
Report bugs at https://github.com/tidyverse/hms/issues


Although many aspects of a phrase may be illustrated by applying it to scalar arguments or arrays of successive integers, more thorough explorations require the use of several classes of arrays, including negative, fractional, complex, alphabetic, and boxed. Moreover, the use of only systematic arguments such as i. 6 and i. 2 3 4 might suggest that a phrase possesses properties that it does not when applied to a random array such as ?. 2 3 4$10 .
The phrases of this section provide a variety of specimen arrays. Because they will be used in illustrating the application of phrases in many different sections, they should perhaps be loaded into the special z locale, so as to be generally available, either in the form d0_z_ or d0
| d0=: QI=: ?.@$ | Shape x of integers from i. y |
| d1=: QD=: ?.@(,:~)@$ | Shape 2,x of integers from i. y |
| d2=: QN=: -/@QD | Random negative and positive integers |
| d3=: QF=: %/@QD | Random fractions |
| d4=: QC=: j./@QD | Random complex numbers |
| d5=: QA=: {&a.@(a.&i.@]+[QI 26"_) | Random literal (26 beginning at y.) |
| m6=: QI&10 | Array of random single-digit integers |
| m7=: QZ=: QI&1 | Array of zeros |
| m8=: QB=: QI&2 | Array of Booleans |
Random arrays can be produced by expressions of the form ? 2 3 4$10 , but for repeatable experiments it is necessary to use a specific random seed, as provided by the function ?. , and in any other phrase that uses it. For example:
(QA&'A' ; QA&'a' ; QA&'+') 2 3 4 +----+----+----+ |DTLN|dtln|.>68| |FBRR|fbrr|0,<<| |YJNV|yjnv|C48@| | | | | |ABNR|abnr|+,8<| |AJBK|ajbk|+4,5| |RPYW|rpyw|<:CA| +----+----+----+
The following phrases may be applied to box an argument, to scale and shift it to a specified range, or to normalize it to the range 0 to 1:
| a9=: B=: 1 : '<"x.' | Adverb: k B is box of rank k |
| m10=: B0=: 0 B | Box atoms |
| m11=: B1=: 1 B | Box vectors (lists) |
| m12=: B2=: 2 B | Box matrices (tables) |
| m13=: RG=: (1: + >./ - <./)@, | Range of values of y |
| d14=: SC=: * % RG@] | Scale range of y to scalar x |
| m15=: NR=: 1&SC | Normalize range to 1 |
| m16=: SZ=: - <./@, | Shift to make smallest value zero |
| m17=: NM=: SZ@NR | Normalize to range 1 and min value 0 |
B0 B1 2 3 4 QA 'A' +--------------------+ |+----+|+----+|+----+| ||DTLN|||FBRR|||YJNV|| |+----+|+----+|+----+| +------+------+------| |+----+|+----+|+----+| ||ABNR|||AJBK|||RPYW|| |+----+|+----+|+----+| +--------------------+ B2 3 4 5 QA 'a' +-----+-----+-----+ |dtlnf|rpywn|zstqb| |brryj|crksx|qxhlt| |nvabn|tgbti|mghje| |rajbk|qtzjg|mxxbx| +-----+-----+-----+ (] ; NR ; SZ ; NM) 3 4 QN 10 +----------+---------------+-------+---------------+ |_4 2 _1 0|1.6 0.4 0.1 0|1 7 4 5|0.1 0.7 0.4 0.5| |_3 _5 1 1|0.9 2.5 0.1 0.1|2 0 6 6|0.2 0 0.6 0.6| | 4 _2 0 3|1.6 0.4 0 0.9|9 3 5 8|0.9 0.3 0.5 0.8| +----------+---------------+-------+---------------+ ] Scale range to 1 Shift to 0 min Norm pos and range


--- title: "London 2019" layout: splash classes: - landing - dark-theme ---
This is our third Map Camp in London and it is kindly sponsored by the Leading Edge Forum. Map Camp is a community run event, it consists of a series of lectures from renowned speakers with a coffee and networking area to meet other mappers from across the globe. We have a wonderful line up of speakers which will be announced shortly. The original event in 2017 was planned to be 30 people, we ended up with 180 and over 150 on a waiting list. In 2018, we planned for 350 people, we ended up with 450 and over 260 on a waiting list. This year, we have an even bigger venue in the hope that we might be able to accommodate everyone.
Map Camp is a safe environment for all and we will do everything we can to make sure that it is. If you're planning on attending please read the code of conduct.
We will start publishing the schedule over the next few weeks. Early bird tickets are limited to the first 300. We do run a diversity campaign and we aim for not only 50% participation of men and women in the speaker line-up but attendees. For the last two years, we've achieved a 50% balance in speakers but attendance has been around 30% - 35% women. We will be doubling our efforts this year and extending the programme further.


This .pager class provides previous and next buttons (links):
.pager
The .previous and .next classes align each link to the sides of the page:
.previous
.next


Kuber:
That feels so good! I ordered a boiled egg to go with my drink! Once the liquor and the egg arrive, I'll be in heaven!
"I'm here to deliver some liquor."


|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
| Interface Summary | |
|---|---|
| ACTIVE | org/omg/PortableInterceptor/ACTIVE.java . |
| ClientRequestInfo | Request Information, accessible to client-side request interceptors. |
| ClientRequestInfoOperations | Request Information, accessible to client-side request interceptors. |
| ClientRequestInterceptor | Client-side request interceptor. |
| ClientRequestInterceptorOperations | Client-side request interceptor. |
| Current | Portable Interceptors Current (also known as PICurrent )
is merely a slot table, the slots of which are used by each service to
transfer their context data between their context and the request's or
reply's service context. |
| CurrentOperations | Portable Interceptors Current (also known as PICurrent )
is merely a slot table, the slots of which are used by each service to
transfer their context data between their context and the request's or
reply's service context. |
| DISCARDING | org/omg/PortableInterceptor/DISCARDING.java . |
| HOLDING | org/omg/PortableInterceptor/HOLDING.java . |
| INACTIVE | org/omg/PortableInterceptor/INACTIVE.java . |
| Interceptor | All Portable Interceptors implement Interceptor. |
| InterceptorOperations | All Portable Interceptors implement Interceptor. |
| IORInfo | Provides the server-side ORB service with access to the applicable policies during IOR construction and the ability to add components. |
| IORInfoOperations | Provides the server-side ORB service with access to the applicable policies during IOR construction and the ability to add components. |
| IORInterceptor | Interceptor used to establish tagged components in the profiles within an IOR. |
| IORInterceptor_3_0 | org/omg/PortableInterceptor/IORInterceptor_3_0.java . |
| IORInterceptor_3_0Operations | org/omg/PortableInterceptor/IORInterceptor_3_0Operations.java . |
| IORInterceptorOperations | Interceptor used to establish tagged components in the profiles within an IOR. |
| LOCATION_FORWARD | org/omg/PortableInterceptor/LOCATION_FORWARD.java . |
| NON_EXISTENT | org/omg/PortableInterceptor/NON_EXISTENT.java . |
| ObjectReferenceFactory | The object reference factory. |
| ObjectReferenceTemplate | The object reference template. |
| ORBInitializer | Facilitates interceptor registration and ORB initialization. |
| ORBInitializerOperations | Facilitates interceptor registration and ORB initialization. |
| ORBInitInfo | Passed to each ORBInitializer , allowing it to
to register interceptors and perform other duties while the ORB is
initializing. |
| ORBInitInfoOperations | Passed to each ORBInitializer , allowing it to
to register interceptors and perform other duties while the ORB is
initializing. |
| PolicyFactory | Enables policy types to be constructed using
CORBA.ORB.create_policy . |
| PolicyFactoryOperations | Enables policy types to be constructed using
CORBA.ORB.create_policy . |
| RequestInfo | Request Information, accessible to Interceptors. |
| RequestInfoOperations | Request Information, accessible to Interceptors. |
| ServerRequestInfo | Request Information, accessible to server-side request interceptors. |
| ServerRequestInfoOperations | Request Information, accessible to server-side request interceptors. |
| ServerRequestInterceptor | Server-side request interceptor. |
| ServerRequestInterceptorOperations | Server-side request interceptor. |
| SUCCESSFUL | org/omg/PortableInterceptor/SUCCESSFUL.java . |
| SYSTEM_EXCEPTION | org/omg/PortableInterceptor/SYSTEM_EXCEPTION.java . |
| TRANSPORT_RETRY | org/omg/PortableInterceptor/TRANSPORT_RETRY.java . |
| UNKNOWN | org/omg/PortableInterceptor/UNKNOWN.java . |
| USER_EXCEPTION | org/omg/PortableInterceptor/USER_EXCEPTION.java . |
| Class Summary | |
|---|---|
| AdapterManagerIdHelper | Adapter manager identifier. |
| AdapterNameHelper | The name of an object adapter. |
| AdapterStateHelper | Type of object adapter state. |
| CurrentHelper | Portable Interceptors Current (also known as PICurrent )
is merely a slot table, the slots of which are used by each service to
transfer their context data between their context and the request's or
reply's service context. |
| ForwardRequestHelper | org/omg/PortableInterceptor/ForwardRequestHelper.java . |
| InvalidSlotHelper | org/omg/PortableInterceptor/InvalidSlotHelper.java . |
| IORInterceptor_3_0Helper | org/omg/PortableInterceptor/IORInterceptor_3_0Helper.java . |
| IORInterceptor_3_0Holder | org/omg/PortableInterceptor/IORInterceptor_3_0Holder.java . |
| ObjectIdHelper | Type of an object id. |
| ObjectReferenceFactoryHelper | The object reference factory. |
| ObjectReferenceFactoryHolder | The object reference factory. |
| ObjectReferenceTemplateHelper | The object reference template. |
| ObjectReferenceTemplateHolder | The object reference template. |
| ObjectReferenceTemplateSeqHelper | Sequence of object reference templates is used for reporting state changes that do not occur on the adapter manager. |
| ObjectReferenceTemplateSeqHolder | Sequence of object reference templates is used for reporting state changes that do not occur on the adapter manager. |
| ORBIdHelper | org/omg/PortableInterceptor/ORBIdHelper.java . |
| ServerIdHelper | org/omg/PortableInterceptor/ServerIdHelper.java . |
| Exception Summary | |
|---|---|
| ForwardRequest | org/omg/PortableInterceptor/ForwardRequest.java . |
| InvalidSlot | org/omg/PortableInterceptor/InvalidSlot.java . |
Provides a mechanism to register ORB hooks through which ORB services can intercept the normal flow of execution of the ORB.
IORInterceptor
-
Used to establish tagged components in the profiles within an IOR.ClientRequestInterceptor
-
Intercepts the flow of a request/reply sequence through the ORB on
the client side.ServerRequestInterceptor
-
Intercepts the flow of a request/reply sequence through the ORB on
the server side.
ORBInitializer
for how to go about registering interceptors.
org.omg.PortableInterceptor
-
RequestInfo
- sync_scope(): Always returns SYNC_WITH_TRANSPORT
- arguments(): Only supported for DII/DSI calls
- exceptions(): Only supported for DII calls on client side.
Not supported on server-side.
- contexts(): Only supported for DII calls on client side.
Not supported on server-side since our ORB does not send contexts.
- operation_context(): Only supported for DII calls
on client side. Not supported on server-side since ORB
does not send contexts.
- result(): Only supported for DII/DSI calls
-
ClientRequestInfo
- received_exception_id(): Always returns null in the
DII case
- get_request_policy(int type): Not implemented.
-
ServerRequestInfo
- sending_exception(): Does not support user exceptions on
the server side in non-DSI case.
For a precise list of supported sections of official OMG specifications with which the Java 2 Platform, Standard Edition, v.1.4 complies, see Official Specifications for CORBA support in J2SE 1.4.
|
JavaTM 2 Platform Standard Ed. 5.0 |
|||||||||
| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
Copyright © 2004, 2010 Oracle and/or its affiliates. All rights reserved. Use is subject to license terms. Also see the documentation redistribution policy.


Now you are ready to start interacting, experimenting, and building with your application. For further instructions on how to run and interact with your booster, please refer to the documentation that is attached.


{% extends 'base.html' %} {% block title %}Welcome to the Lending Library{% endblock %} {% block headextra %} {% endblock %} {% block body %}
Have a book you'd like to lend? Looking for something good to read? That's what we're here for. Check out the latest titles added by your teammates, and add some yourself!
{% include 'message_alerts.html' %}