src.predictive_model.classification package¶
Subpackages¶
Submodules¶
src.predictive_model.classification.apps module¶
-
class
src.predictive_model.classification.apps.
ClassificationConfig
(app_name, app_module)¶ Bases:
src.predictive_model.apps.PredictiveModelConfig
-
name
= 'src.predictive_model.classification'¶
-
src.predictive_model.classification.classification module¶
-
src.predictive_model.classification.classification.
classification
(training_df, test_df, clusterer, job)¶ main classification entry point
train and tests the classifier using the provided data
Parameters: - clusterer (
Clustering
) – - training_df (
DataFrame
) – training DataFrame - test_df (
DataFrame
) – testing DataFrame - job (
Job
) – job configuration
Return type: (<class ‘dict’>, <class ‘dict’>)
Returns: predictive_model scores and split
- clusterer (
-
src.predictive_model.classification.classification.
predict
(job, data)¶ Return type: Any
-
src.predictive_model.classification.classification.
update_and_test
(training_df, test_df, job)¶
src.predictive_model.classification.custom_classification_models module¶
-
class
src.predictive_model.classification.custom_classification_models.
NNClassifier
(**kwargs)¶ Bases:
sklearn.base.ClassifierMixin
Neural Network classifier, implements the same methods as the sklearn models to make it simple to add
-
fit
(train_data, targets)¶ creates and fits the predictive_model
first the encoded data is parsed, then the predictive_model created and then trained
Parameters: - train_data (
DataFrame
) – encoded training dataset - targets (
ndarray
) – encoded target dataset
Return type: None
- train_data (
-
predict
(test_data)¶ returns predictive_model predictions
parses the encoded test dataset, then returns the predictive_model predictions
Parameters: test_data ( DataFrame
) – encoded test datasetReturn type: ndarray
Returns: predictive_model predictions
-
predict_proba
(test_data)¶ returns the classification probability
parses the test dataset and returns the raw prediction probabilities of the predictive_model
Parameters: test_data ( DataFrame
) – encoded test datasetReturn type: ndarray
Returns: predictive_model prediction probabilities
-
reset
()¶ placeholder to allow use with other sklearn algorithms
Return type: None
-
src.predictive_model.classification.methods_default_config module¶
-
src.predictive_model.classification.methods_default_config.
classification_decision_tree
()¶
-
src.predictive_model.classification.methods_default_config.
classification_incremental_adaptive_tree
()¶
-
src.predictive_model.classification.methods_default_config.
classification_incremental_hoeffding_tree
()¶
-
src.predictive_model.classification.methods_default_config.
classification_incremental_naive_bayes
()¶
-
src.predictive_model.classification.methods_default_config.
classification_incremental_perceptron
()¶
-
src.predictive_model.classification.methods_default_config.
classification_incremental_sgd_classifier
()¶
-
src.predictive_model.classification.methods_default_config.
classification_knn
()¶
-
src.predictive_model.classification.methods_default_config.
classification_nn
()¶
-
src.predictive_model.classification.methods_default_config.
classification_random_forest
()¶
-
src.predictive_model.classification.methods_default_config.
classification_xgboost
()¶
src.predictive_model.classification.models module¶
-
class
src.predictive_model.classification.models.
AdaptiveHoeffdingTree
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, grace_period, split_criterion, split_confidence, tie_threshold, remove_poor_atts, leaf_prediction, nb_threshold)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_leaf_prediction_display
(*, field=<django.db.models.fields.CharField: leaf_prediction>)¶
-
get_split_criterion_display
(*, field=<django.db.models.fields.CharField: split_criterion>)¶
-
grace_period
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
leaf_prediction
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
nb_threshold
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
remove_poor_atts
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
split_confidence
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
split_criterion
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
tie_threshold
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
Classification
(*args, **kwargs)¶ Bases:
src.predictive_model.models.PredictiveModel
Container of Classification to be shown in frontend
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.models.MultipleObjectsReturned
-
adaptivehoeffdingtree
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
decisiontree
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
hoeffdingtree
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
static
init
(configuration)¶ Return type: PredictiveModel
-
knn
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
naivebayes
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
neuralnetwork
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
perceptron
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
predictivemodel_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
predictivemodel_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
randomforest
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
sgdclassifier
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
xgboost
¶ Accessor to the related object on the reverse side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Place.restaurant
is aReverseOneToOneDescriptor
instance.
-
exception
-
class
src.predictive_model.classification.models.
ClassificationMethods
¶ Bases:
enum.Enum
An enumeration.
-
ADAPTIVE_TREE
= 'adaptiveTree'¶
-
DECISION_TREE
= 'decisionTree'¶
-
HOEFFDING_TREE
= 'hoeffdingTree'¶
-
KNN
= 'knn'¶
-
MULTINOMIAL_NAIVE_BAYES
= 'multinomialNB'¶
-
NN
= 'nn'¶
-
PERCEPTRON
= 'perceptron'¶
-
RANDOM_FOREST
= 'randomForest'¶
-
SGDCLASSIFIER
= 'SGDClassifier'¶
-
XGBOOST
= 'xgboost'¶
-
-
class
src.predictive_model.classification.models.
DecisionTree
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, max_depth, min_samples_split, min_samples_leaf)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
max_depth
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
min_samples_leaf
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
min_samples_split
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
HoeffdingTree
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, grace_period, split_criterion, split_confidence, tie_threshold, remove_poor_atts, leaf_prediction, nb_threshold)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_leaf_prediction_display
(*, field=<django.db.models.fields.CharField: leaf_prediction>)¶
-
get_split_criterion_display
(*, field=<django.db.models.fields.CharField: split_criterion>)¶
-
grace_period
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
leaf_prediction
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
nb_threshold
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
remove_poor_atts
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
split_confidence
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
split_criterion
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
tie_threshold
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
Knn
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, n_neighbors, weights)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_weights_display
(*, field=<django.db.models.fields.CharField: weights>)¶
-
n_neighbors
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
weights
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
exception
-
class
src.predictive_model.classification.models.
NaiveBayes
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, alpha, fit_prior)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
alpha
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
fit_prior
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
NeuralNetwork
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, n_hidden_layers, n_hidden_units, activation, n_epochs, dropout_rate)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
activation
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
dropout_rate
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_activation_display
(*, field=<django.db.models.fields.CharField: activation>)¶
-
n_epochs
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
Perceptron
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, penalty, alpha, fit_intercept, tol, shuffle, eta0, validation_fraction, n_iter_no_change)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
alpha
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
eta0
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
fit_intercept
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_penalty_display
(*, field=<django.db.models.fields.CharField: penalty>)¶
-
n_iter_no_change
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
penalty
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
shuffle
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
tol
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
validation_fraction
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
exception
-
class
src.predictive_model.classification.models.
RandomForest
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, n_estimators, max_depth, max_features)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
max_depth
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
max_features
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
n_estimators
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception
-
class
src.predictive_model.classification.models.
SGDClassifier
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, loss, penalty, alpha, l1_ratio, fit_intercept, tol, epsilon, learning_rate, eta0, power_t, n_iter_no_change, validation_fraction, average)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
alpha
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
average
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
epsilon
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
eta0
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
fit_intercept
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
get_learning_rate_display
(*, field=<django.db.models.fields.CharField: learning_rate>)¶
-
get_loss_display
(*, field=<django.db.models.fields.CharField: loss>)¶
-
get_penalty_display
(*, field=<django.db.models.fields.CharField: penalty>)¶
-
l1_ratio
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
learning_rate
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
loss
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
n_iter_no_change
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
penalty
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
power_t
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
tol
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
validation_fraction
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
exception
-
class
src.predictive_model.classification.models.
XGBoost
(id, model_path, predictive_model, prediction_method, predictivemodel_ptr, classification_ptr, n_estimators, max_depth)¶ Bases:
src.predictive_model.classification.models.Classification
-
exception
DoesNotExist
¶ Bases:
src.predictive_model.classification.models.DoesNotExist
-
exception
MultipleObjectsReturned
¶ Bases:
src.predictive_model.classification.models.MultipleObjectsReturned
-
classification_ptr
¶ Accessor to the related object on the forward side of a one-to-one relation.
In the example:
class Restaurant(Model): place = OneToOneField(Place, related_name='restaurant')
Restaurant.place
is aForwardOneToOneDescriptor
instance.
-
classification_ptr_id
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
max_depth
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
n_estimators
¶ A wrapper for a deferred-loading field. When the value is read from this object the first time, the query is executed.
-
to_dict
()¶
-
exception


@(loggedin: Boolean, form: DynamicForm)
Pathfinder is a logistics platform for shared transportation and delivery applications. We provide real-time route computation and updates as transportation requests come in and vehicles go on and offline. Pathfinder is a logistics platform for transportation and delivery applications. We provide real-time route computation and optimization as your users request transportation.
Instead of programming directly against our websocket API, you can use our developer-friendly client libraries to interact with the Pathfinder platform.
Transportation requests, vehicle creation, status changes and route updates are pushed to clients via websockets in real-time.
Transports and commodities are grouped into clusters that are routed separately. This gives you granular control over the routes for your application.
The admin dashboard allows you to configure capacities, parameters and custom objective functions that configure the route computation process.
To get your application up and running fast, we've created several tutorials on using Pathfinder as well as a cross-platform sample application. You can check these out in our comprehensive documentation suite.
Pathfinder was developed by a team of students at Rose-Hulman Institute of Technology over the course of the 2015-2016 school year. You can reach us at info@@thepathfinder.xyz.
All of our code is MIT licensed and available on Github.


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{% trans "Simulated Image" %} {% trans "Original Image" %} {% trans "Output Image" %} {% trans "Your browser does not support the HTML5 canvas tag. The following image is not the real output." %}


--- layout: archive title: Zulip Chat Archive permalink: /stream/113488-general/topic/.60hint.60.20tactic.html ---
The hint
tactic has landed in master. It tries out a bunch of tactics, and reports which ones make some progress against the current goal.
I'm hoping it's particularly useful for beginners and people learning what tactics are available.
(Although there are some classes of tactics that it doesn't make sense to have hint
suggest, because they're always available: have
, revert
, etc.)
It's pretty easy to change which tactics are tried: in fact you can just run add_hint_tactic "my_tactic"
and it will be added to the list.
We almost certainly should tweak the list of things it tries by default, so any reports of unexpected behaviour, or obviously missing useful suggestions, would be great.
Any reason not to use an attribute instead of add_hint_tactic
?
Just because often the thing you want to put in the quotes is actually some interactive tactic, whose underlying definition takes various optional arguments, etc.
So we're relying on the parser to run whatever it is that turns up in the quotes, rather than trying to wrangle a complicated tactic definition type signature into the tactic unit
that we want.
I see what you mean. Do you customize the argument list for tactics like simp
?
At the moment it is just add_tactic_hint "simp at *"
. Ideally we would actually hook this up with squeeze_simp
, and print a detailed simp only ...
string.
(Instead of using add_hint_tactic
, we can just add the hint_tactic
attribute to any tactic string
, so we'd just need to provide a tactic string
valued wrapper for squeeze_simp
.)
It will be interesting to see how the undergraduates use this. Thanks!
Yes --- particularly helpful will be reports of students getting stuck _beccause_ they tried hint
and didn't receive a useful suggestion...
Last updated: Apr 24 2020 at 19:02 UTC


combinations
added to sum_network_routes()
so it runs quicker - see pull/177.sum_network_routes()
, weightfield()
and find_network_nodes()
- see e.g. example(sum_network_routes)
for details.l_poly
added.SpatialLinesNetwork()
fixed._each()
dplyr functions replaced with equivalent _at
or _all
functions. See here for more.vignette("stplanr-paper")
for details.introducing-stplanr
vignette has been updated. It now provides a more basic introduction for people new to R for spatial and transport data.line2route()
has been refactored to improve error detection and allow n_processes
arguments. Thanks @nikolai-b. See pull/151 for details.line_match()
function added, a wrapper around rgeos::gDistance()
, to find similar routes.od_aggregate()
and sp_aggregate()
have been added, to enable OD data to be aggregated to new geographic levels.#141
fixed: viaroute()
works again.bidirectional = TRUE
returns a different result in line_bearing()
now.onewayid()
is now a generic function, meaning it can handle spatial and non-spatial dataline2route()
allow you to specify variables to join-by - also has updated and more sensible defaultsod_id_order()
to put origin-destination ids in order, to identify 2 way duplicates (split out from onewayid()
)route_cyclestreet()
leading change_elev
and av_incline
being wrong now fixedNEW FEATURES
od2line()
. See example(od2line)
for an example.destinations
for showing how OD matrix with destinations can be converted to spatial datalist_output
allows the route information to be saved as a list, allowing save_raw = TRUE
(which does not return a Spatial
object) to be passed to the route_
function.BUG FIXES
line2route()
(#124) fixedNEW FEATURES
New function reproject()
is a simple wrapper around spTransform()
that uses crs_select_aeq()
to convert a spatial object in geographic (lat/lon) coordinates into on with projected coordinates, with units of 1 m. This is useful for various spatial operations, such as finding the length and area of an object.
Implement gprojected()
, a function for performing GIS operations on a temporary, projected, version of spatial objects.
Addition of line_bearing()
to return the bearing of lines based on start and end points.
Addition of angle_diff()
for finding the angular difference between lines: are they roughly parallel or perpendicular?
BUG FIXES
NEW FEATURES
Update to OSRM functions to support API v5.
New parameter byvars
in the overline()
function, to allow disaggregation of results by a grouping variable (see example(overline)
).
Faster implementation of od2line()
: od2line2()
. Plan is to replace the original if no issues are found with new implementation.
New function od2odf()
which converts OD data into a dataframe of origins and destinations (feeds od2line2()
but also useful as self-standing function).
New argument new_proj
in buff_geo()
allows the results to be exported to any coordinate reference system (CRS).
New function gprojected()
generalises concept of buff_geo()
, building on crs_select_aeq()
to allow any GIS query to be conducted on a temporary projected version of spatial objects with geographical CRSs.
New function od_dist()
can quickly calculate Euclidean distances of OD pairs without converting to spatial objects.
BUG FIXES
Bug fix in onewayid()
so it captures all lines.
Various improvements to documentation and code.
NEW FEATURES
Interface to the Google Distance Matrix API with dist_google
.
New transport planning API added, with route_transportapi_public
(for testing).
Update to line2route
, allowing it to accept different routing funtions via the new argument route_fun
(for testing - tested with route_fun = route_cyclestreet
).
New functions for creating origin-destination data frames (point2odf
) and SpatialLinesDataFrames (points2flow
).
Addition of n_vertices
and is_linepoint
for identifying the number of vertices in spatial objects and whether the ‘line’ is really a point.
BUG FIXES
line2route
refactored, with 10 fold speed increases on large (1000+) batches of lines.NEW FEATURES
Addition of new class definition SpatialLinesNetwork
, methods for plot
and summary
and functions calc_network_routes
and find_network_nodes
allowing fast route calculations via igraph and other network analysis functions.
Functions for removing beginning and end of lines: toptail
and toptailgs
. Helper functions buff_geo
, crs_select_aeq
and line2points
added.
Functionality for reading in the UK’s stats19 data: read_stats19_*
functions download, unzip and re-categorise the data.
read_table
functions added for reading Australian OD data.
decode_gl
added to decode Google polylines and other functions for querying and reading data from OSRM services.
gtfs2sldf
added to import GTFS routes as SpatialLinesDataFrames.


{% extends "base.html" %} {% block title %}
Medieval catalogues > AUGUSTINIAN CANONS > Osney > Leland, c. 1536-40
{% endif %}7 identified entries found.
All data was derived from the List of Identifications by Professor Richard Sharpe.
{% if not printing %}
A key to codes used in the List is available (opens in new tab).{% endif %}


MojitProxy Class
The object that is given to each mojit binder to be used to interact with other mojits and the mojito framework.
Item Index
Methods
Methods
broadcast
-
name
-
payload
-
options
Used by mojit binders to broadcast a message between mojits.
Parameters:
-
name
Stringevent name.
-
payload
Objectthe payload for the event.
-
options
Objectcurrently only used to specify target for broadcast. For example, to target only one child mojit for broadcast, use: {target: {slot: 'slot name', viewId: 'DOM view id'}}.
data.get
-
name
Returns the value of the specified attribute.
Parameters:
-
name
StringAttribute name.
Returns:
undefined
if the attribute doesn't
exist.
data.set
-
name
-
value
-
[options]
Sets the value of a single attribute.
Parameters:
-
name
StringAttribute name.
-
value
AnyValue to set.
-
[options]
Object optionalData to be mixed into the event facade of the
change
event(s) for these attributes.-
[silent=false]
Boolean optionalIf
true
, nochange
event will be fired.
-
destroyChild
-
id
-
retainNode
Clears out a child's view, calling the appropriate life cycle functions, then destroy's its binder and dereferences it. Will also dereference the child from this mojit's children.
Parameters:
-
id
StringEither the slot key of the child, or the DOM view id of the child.
-
retainNode
Booleanif true, the binder's node will remain in the dom.
destroyChildren
-
retainNode
Destroys all children. (Calls destroyChild() for each child.)
Parameters:
-
retainNode
Booleanif true, the binder's node will remain in the dom.
destroySelf
-
retainNode
Allows a binder to destroy itself and be removed from Mojito client runtime entirely.
Parameters:
-
retainNode
Booleanif true, the binder's node will remain in the dom.
getChildren
()
Object
Helper function to gather up details about a mojit's children from the Mojito Client.
Returns:
getFromUrl
-
key
Gets URL parameters
Parameters:
-
key
StringThe name of the parameter required.
Returns:
invoke
-
action
-
options
-
cb
Used by the mojit binders to invoke actions on themselves within Mojito. The options parameter is optional and may contain:
- params
- <object> must be broken out explicitly:
- route
- <object> Map of key/value pairs.
- url
- <object> Map of key/value pairs.
- body
- <object> Map of key/value pairs.
- file
- <object> Map of key/value pairs.
- rpc
- <boolean> Means that we are immediately sending the request to the server to answer the invocation.
Parameters:
-
action
Stringname of the action to invoke.
-
options
Objectsee above.
-
cb
Functionfunction to be called on completion.
listen
-
name
-
callback
Allows mojit binders to register to listen to other mojit events
Parameters:
-
name
Stringevent name.
-
callback
Functioncalled when an event is broadcast with the event data.
pageData.get
-
name
Returns the value of the specified attribute.
Parameters:
-
name
StringAttribute name.
Returns:
undefined
if the attribute doesn't
exist.
pageData.set
-
name
-
value
-
[options]
Sets the value of a single attribute.
Parameters:
-
name
StringAttribute name.
-
value
AnyValue to set.
-
[options]
Object optionalData to be mixed into the event facade of the
change
event(s) for these attributes.-
[silent=false]
Boolean optionalIf
true
, nochange
event will be fired.
-
refreshView
-
opts
-
cb
Refreshes the current DOM view for this binder without recreating the binder instance. Will call the binder's onRefreshView() function when complete with the new Y.Node and HTMLElement objects.
Parameters:
-
opts
Objectsame as the options for invoke().
-
cb
FunctionCalled after replacement and onRefreshView have been called, sends data/meta.
render
-
data
-
view
-
cb
This method renders the "data" provided into the "View" specified. The "view" must be the name of one of the files in the current Mojits "views" folder. Returns via the callback.
Parameters:
-
data
ObjectThe data to render.
-
view
StringThe view name to use for rendering.
-
cb
Function(err,str)The callback function.
unlisten
-
[optional]
The opposite of the "listen" function. Deletes all callback functions from the listener queue associated with this binder and event type. If event name is not specified, all callbacks associated with this binder are deleted.
Parameters:
-
[optional]
String optionalname event name.
Properties
config
Object
The mojit configuration for this binder
context
Object
The context used to generate this page
data
Model.Vanilla
The mojit data model, for getting and setting data that is shared by a single mojit controller, binder, and/or template. Use this.data.get() and this.data.set(). See also http://yuilibrary.com/yui/docs/api/classes/Model.html and http://developer.yahoo.com/cocktails/mojito/docs/topics/mojito_data.html#sharing-data
pageData
Model.Vanilla
The page data model, or getting and setting data that is shared between mojits on a page. Use this.pageData.get() and this.pageData.set(). See also http://yuilibrary.com/yui/docs/api/classes/Model.html and http://developer.yahoo.com/cocktails/mojito/docs/topics/mojito_data.html#sharing-data
proxied
Object
The config of a child which this mojit is proxying.
type
String
The mojit type


Push me out is a very basic prototype that sends push notification to a browser using the Push Service and Service Worker. This application lets you send customised notification to subscribers.
The application is divided into two systems:
- Client -- (Javascript, jQuery, Bootstrap)
- Server -- (Python, Django, Postgres)
- Generates The Push Service's endpoint and sends the credentials to the server
- Handles the push events when the server sends the push events
- Stores the subscription info (push service endpoint, auth keys)
- Triggers the push event to Push Service by calling the push service endpoint
Boostraping Application
- Registers the Service Worker when the page is loaded
- Once the service worker is registered successfully, the client subscribes for push event
- At this point, if the permission for receiving push events is not granted, the browser asks for the permission
- Once the permission is granted, push service endpoint is created and sent to the backend server
Storing Subscriber
- The server recieves the push service credentials (push service endpoint, auth keys)
- If these credentials are not already present, server stores them in a persistent storage
- If the credentials are already stored with the server, server throws a 400 bad request message
Triggering Notification
- For triggering notification, the client hits the server endpoint that triggers the notification
- If the client sends the subscriber_id, then only that subscriber is notified
- If no subscriber_id is sent to the server, all the subscribers are notified
- After receiving the request, server registers the request and fires an asynchronous task to trigger push event
- After firing an asynchronous task, server returns the response if the server successfully registers the request
Receiving Notification
- After the server triggers the push event, the push service redirects the event to the appropriate client
- Service Worker listens to the push event, once the push event is received by the browser, the service worker is invoked
- Service Worker then shows the notification according to the data sent by the server


 This is the moral dilemma placed before Sheriff (Vince Vaughn) and Tony (David Conrad) in Return To Paradise.
Two years previous Sheriff, Tony, and Lewis (Joaquin Phoenix) are three young guys that meet by coincidences unexplained, in Malaysia. They become best of friends and live together for five weeks partying and having a great time. Finally, Tony and Sheriff decide they have to get back to the states, they exchange good-byes and go back to their humdrum lives in the states.
Two years later, they find out, that Lewis, who stayed in Malaysia to work on environmental projects, was arrested the day after their departure. It seems that the police had come to their house in search of a missing bike, and instead found a block of hash. Due to the amount of weed found, Lewis is charged with trafficking and sentenced to be hanged.
After two years of appeals, in desperation, Lewis sends his lawyer Beth Eastern (Anne Heche) to attempt to persuade his two friends to return to Malaysia and accept responsibility for their portions of the pot. If they both return, Lewis' life will be spared and they will both be given three years and if only one returns he will get six years imprisonment.
The film mostly follows Sheriff, now a limousine driver in NYC, as he tries to decide if he should return and give up his freedom. Also featured is Jada Pinkett in a small role as an ambitious and deceitful newspaper reporter, named M.J.
Return to Paradise is beautifully directed by Joseph Ruben, whose previous work includes "The Good Son". He does a great job of keeping the pace of the film, and doesn't let up near the end, but unleashes several great plot twist. The script is suspenseful, and was tightly written by Wesley Strick and Bruce Robinson, with lots of great dialog.
This is a very powerful, intelligent, and thought provoking film. It contains great performances by Vince Vaughn and Anne Heche. I was especially impressed by Heche's performance, as it is so much stronger than all her previous roles.
It is rare, in the summer, to find a movie that makes the audience think. This one does. There wasn't one person who didn't leave the theater, who wasn't thinking to themselves, "Would I do this for my friend." As for the answer, they probably will have a of hard time with it as these characters did.
Grade: A
----------------- Ryan Davis<EMAIL_ADDRESS>-----== Posted via Deja News, The Leader in Internet Discussion ==----- http://www.dejanews.com/rg_mkgrp.xp Create Your Own Free Member Forum
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


|
H8Synthe
|
This file contains the implementation of modules which are Synthesizer. [詳解]
関数 | |
| void | main (void) |
This file contains the implementation of modules which are Synthesizer.
Copyright (c) 2008 K&J Software Design, Ltd All rights reserved.
synthesizer.c に定義があります。


{% extends "books_app/base.html" %} {% block content %}
Designed by K Srinidhi Bhat Inspired by the Django tutorial from Corey Schafer


Sure, we GAN!
Image-to-image translation methods for the win
Different flavours of domain adaptation
Source
Target
Unsupervised domain adaptation
Fully labeled
Fully unlabeled
Semi-supervised domain adaptation
Fully labeled
Partially labeled
Few-shot domain adaptation
Fully labeled
Few samples
Different flavours of image-to-image translation
Presence of pairs
Supervised i2i translation
Yes
Unsupervised i2i translation
No
Supervised image-to-image translation
Supervision - correspondence between samples in the datasets
pix2pix - https://phillipi.github.io/pix2pix/
Conditional GAN
source sample passed to the generator
the discriminator sees pairs: corresponding source & target images
Unsupervised image-to-image translation
More challenging - relation between datasets not present in the data
But also more interesting - practically often impossible to get create datasets with one-to-one correspondences
Synthetic images
Real images
What is the relation between images from different domains?
Classical model of domain adaptation
(Ben David et al., 2010)
Probabilistic perspective : we consider
two distributions $P(X_s, Y_s)$ and $P(X_t, Y_t)$ for
source and target samples/labels
Discrepency between the domains :
\[
\epsilon_T(h) \leq \epsilon_S(h) + \frac{1}{2} d_{H\Delta H}(X_s, X_t) + \lambda(VC)
\]
where $h \in \mathcal{H}$ is a classifier, $\epsilon_{S/T}$ the error on the source/target distribution and
\[
d_{H\Delta H}(X_s, X_t) = 2 \sup_{h,h' \in \mathcal{H}}
| \mathbb{E}_{x \sim X_s}[h(x) \neq h'(x)] - \mathbb{E}_{x \sim X_t}[h(x) \neq h'(x)] |
\]
Goal : finding $f: \chi_s \rightarrow \chi_t$ to minimize $\frac{1}{2} d_{H \Delta H}(X_s, f(X_t))$
and $h$ to minimize $\epsilon_S(h)$
Classical model of domain adaptation
(Ben David et al., 2010)
\[
d_{H\Delta H}(X_s, X_t) = 2 \sup_{h,h' \in \mathcal{H}}
| \mathbb{E}_{x \sim X_s}[h(x) \neq h'(x)] - \mathbb{E}_{x \sim X_t}[h(x) \neq h'(x)] |
\]
Hypothesis 1
Hypothesis 2
To which extent can we find two hypothesis very similar in one domain,
but very different in the other
Classical model of domain adaptation
But this theoretical distance is hard to compute,
so even harder to minimize with any classicial optimization algorithm...
We have to find other distances
In probability theory, we often use divergences
Definition. Let $S$ be a space of probability distributions.
A divergence $D: S \times S \rightarrow \mathbb{R}$ is a function such that:
$D(P, Q) \geq 0$ for all $P,Q \in S$
$D(P,Q) = 0 \iff P=Q$
Examples: KL-divergence, Wasserstein distance, JS-divergence, etc.
Most DA algorithms consists in choosing a divergence and minimizing it
Optimal Transport
Special case: empirical distribution (uniform distribution on every sample)
(Made with the optimal transport library POT)
$\mathrm{W}(P_r,P_{\theta}) = \inf_{\gamma \in \Pi} \, \sum\limits_{i,j} \Vert x_i - y_j \Vert \gamma (x_i,y_j)$
with $\gamma (x_i,y_j) \in \{0,1\}$
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Joint Distribution Optimal Transportation
(Courty et al., 2017)
(Made with the optimal transport library POT)
Deep JDOT
(Damodaran et al., 2017)
Recent improvements of the first paper:
Learn optimal transport in an embedding space (instead of the input space)
Iterative algorithm:
For a fixed transport plan, learn an embedding network that make the linked points closer, and a classifier on the pseudo-labels
For a fixed embedding and classifier, learn the best transport plan
Adversarial Domain Adaptation
Revolution in domain adaptation starting with Ganin et al., 2015
Using GAN-like deep architectures to minimize the Jensen-Shannon divergence
between our distributions
Adversarial domain adaptation framework:
A conditional generator that takes a target input
and tries to generate a source-like output
A discriminator that tries to separate real source samples
from source-like generated samples
The generator tries to make the discriminator bad
Almost all the recent DA papers are variations on that structure
Adversarial Domain Adaptation
Classical GAN architecture:
Domain Adaptation: replace the input noise by a target image
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Adversarial Domain Adaptation
Toy example: 2D gaussians
Cycle-GAN
(Zhu et al., 2017)
Cycle-consistency assumption
\[
F_{TS}(F_{ST}(x_S)) \approx x_S
\]
It's an approximate assumption - we don't want (can't have) it
UNsupervised Image-to-Image Translation (UNIT)
(Liu et al., 2017)
Hypothesis: shared latent-space
Training: VAE and GAN
UNsupervised Image-to-Image Translation (UNIT)
(Liu et al., 2017)
Your browser does not support the video tag.
UNIT
Your browser does not support the video tag.
Compared to just a target discriminator (no cycle-consistency, no shared latent space)
Extra regularize the latent space
Your browser does not support the video tag.
UNIT
Your browser does not support the video tag.
Beyond UNIT (my results) - regularizing the output space!
State-of-the art algorithm: VADA-DIRT-T
(Shu et al., 2017)
Virtual Adversarial Domain Adaptation (VADA)
Adversarial Method : find an embedding space invariant between the two domains
and a hypothesis that classify the source in this embedding space
Cluster Assumption : the decision-boundary should not cross high-density regions
⇒ the output probability should be extreme (high-confidence)
⇒ $\min_{\theta} \mathbb{E}_{x \in \mathcal{D_t}}[-h_{\theta}(x) \log(h_{\theta}(x))] $
Virtual Adversarial Training : the hypothesis should be invariant to slight perturbation of the input (adversarial examples).
We can minimize the KL divergence between $h_{\theta}(x)$ and $h_{\theta}(x+r)$ for $||r||<\epsilon$
Evolution of domain adaptation results
On the MNIST-SVHN benchmark
SVHN (source) → MNIST (target)


navigation: previous | next | index
Summary of section: Participants will write tests that check the assumptions that they may have about the data that they received. Applied to pre-data analysis, especially with streams of data coming in.
- Write tests for column data types:
int
, float
, str
, object
.
- Write tests that check that there are no unexpected empty data cells: finding
NaN
with pandas
.
- Write tests that check for value correctness (e.g. cannot have negative numbers where log10 transform expected).
- Write tests that check textual data for correct lengths (e.g. biological sequence data should fall within some expected range of lengths).
- Write tests that check for potential outlier data points (e.g. use of Q-test).
- Write tests that check for normality of data (e.g. use of scipy's K-S test, use of K-L divergence).


Locations Latitude Longitude Edit Location Delete Location? @for(i <- 0 until locations.size) { @locations(i).latitude @locations(i).longitude Edit Delete }


|
Patrick NaughtonPatrick Naughton will not be remembered as one of the original geeks who worked for Sun to develop the Java programming language.He will not be fondly recalled for his service to Walt Disney with time spent as vice-president of Infoseek. Instead, people will smirk or frown, depending on the sense of humor possessed, and simply remember Patrick as "Hotseattle". He was an executive in charge of, you know, stuff and things at a Disney funded Internet startup called Go, Michael Eisner's knee-jerk reaction to Yahoo.  All things under the Go Networks umbrella, still sort of in use today, included Disney properties such as Disney Online, ESPN, ABC News and the now-defunct Infoseek search engine.  Depending on who you ask, Patrick spent a little or a lot of time trolling for underage girls on various public Internet Relay Chat networks. The various public IRC networks offered him the opportunity to chickenhawk around in chat rooms named #dads&daughterssex and #girls&oldermen. He initiated conversations with girls, boys, probably some number of strange men and eventually, an FBI agent. Where else but an online chat system can a middle-aged FBI agent pass as a child? Patrick's handle was "Hotseattle" (or "sfmate"). The agent playing the part of a dimwitted teenager chose the nick "krisLA". It took six months for Hotseattle to ask his new friend out on a date. He asked what he thought was a 13-year-old girl, the same one he'd been chatting with for six months, to meet him on the Santa Monica pier for some fun at a local motel. At least he didn't suggest they meet at Disneyland. Patrick had an interesting mating ritual: Apparently the task of convincing a girl to meet you involves sending her a digital photograph of your wang, as Naughton did on several occasions. He described his flights of fantasy as role-playing as himself, pretending to be a successful, rich executive with everything going his way. When he bragged about running a company and owning a boat in chat sessions, he was telling the truth, unlike so many chatters before him. On September 16, 1999, he would learn that his teen fantasy girl was in fact an FBI agent working with Sexual Assault Felony Enforcement agents.  According to the Los Angeles Times, upon being arrested, Patrick blurted out that "there was sexually explicit images of children" on his laptop. In fact, one of the file names was "Baby cunt." A witness from the U.K.'s National Crime Squad identified two of the illegal images as children between the ages of 8 and 9. Naughton claimed his chat software was configured to blindly accept files although his download folder was packed with files. If Mickey cried a little that night then certainly Michael Eisner had a bad day after learning that his young Vice-President in charge of E-mail and Chat Rooms had been arrested for, well, very un-Disneylike behavior. Within a day, Patrick was fired and his biography removed from the company website. Go released this statement: "We are shocked and disturbed" ... "Patrick Naughton is no longer an employee of Infoseek. As this is a pending investigation, there are no further details available at this time. We want to be clear that behavior of this nature is inappropriate and unacceptable by Infoseek and GO Network."  For trial, Naughton's defense team came up with this gem which set legal heads spinning: The chats were all fantasy and that Patrick never intended to have sex with a 13-year-old girl. In fact, it was claimed that he never thought he was chatting with a teenager and instead believed he was chatting with an adult who shared his fantasy. Despite all of the evidence and the decades of prison time hanging over him, Naughton would eventually walk free. The jury in his trial deadlocked over the more serious charge and to avoid a retrial, he plead out to lesser charges. Naughton spent his probation working on an unspecified software project for the FBI. Company sources say the project involved programming to IRC networks. When he was finished with probation, he walked away a free man, not even stopping to fix his open bugs. In the end, Patrick lost everything. The strange case of Patrick Naughton ends with him having spent one (1) weekend in jail, later likened it to a long plane ride. Following the public-relations disaster personified as Patrick, the company that was the Go Network was put to sleep. After losing quite a bit of Disney dollars with nothing to show for it, except for Patrick's presence, the enterprise quietly went out of business. Offices in San Francisco, Sunnyvale, and Seattle were shuttered, employees were sent home with zero-day notice and guards were posted in the lobby, presumably to prevent violence or fax paper theft. This was probably best for Walt Disney, Michael Eisner and Mickey Mouse. Even if this brain fart of an Internet startup somehow wasn't losing millions and millions of dollars, every single future mention of Go in the press would have had to include an obligatory reference to Patrick Naughton. "I'm glad I'm at the top of the food chain." ... "I came from a poor immigrant family. My father came from Ireland, and he basically reproduced Ireland in Rochester. We had ten acres of land with cows and ducks and pigs." -- Patrick Naughton, Forbes article ("Mr. Famous Comes Home"), 1998"The decisions I make change the world — and I've never made a wrong decision in my life." -- Patrick Naughton boasting at a company meeting timeline
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Resident Evil 2, The Outer Worlds, and Sekiro lead The Game Awards nominees
by Dustin BaileyThe Game Awards are nearly upon us once again, and while the big focus will likely be on the new announcements of upcoming games, there’s still a whole awards show there to highlight the year’s biggest and best games. Today, the nominees in all the big categories have been revealed, and voting has opened to the public.
Your six Game of the Year nominees are: Control, Death Stranding, Resident Evil 2, Super Smash Bros. Ultimate, Sekiro: Shadows Die Twice, and The Outer Worlds. (If you’re wondering why a game from 2018 is on the list, Smash came out too late for last year’s vote.) You can head to the official site cast your ballot – while most of the voting power lies with a panel of critics and journalists, fan votes do weight the final decisions.
The Game Awards take place on December 12 at 17:30 PST / 20:30 EST – or December 13 at 1:30 GMT – on YouTube. Loads of other big PC games are among the nominees, like Disco Elysium and Total War: Three Kingdoms, though all the awards remain multiplatform, aside from the mobile category.
Read on for the full list of nominees.
Game of the Year
Control
Death Stranding
Super Smash Bros. Ultimate
Resident Evil 2
Sekiro: Shadows Die Twice
The Outer Worlds
Best Game Direction
Control
Death Stranding
Resident Evil 2
Sekiro: Shadows Die Twice
Outer Wilds
Best Narrative
A Plague Tale: Innocence
Control
Death Stranding
Disco Elysium
The Outer Worlds
Best Art Direction
Control
Death Stranding
Gris
Sayonara Wild Hearts
Sekiro: Shadows Die Twice
The Legend of Zelda: Link’s Awakening
Best Score/Music
Cadence of Hyrule
Death Stranding
Devil May Cry 5
Kingdom Hearts III
Sayonara Wild Hearts
Best Audio Design
Call of Duty: Modern Warfare
Control
Death Stranding
Gears 5
Resident Evil 2
Sekiro: Shadows Die Twice
Best Performance
Ashly Burch as Parvati Holcomb, The Outer Worlds
Courtney Hope as Jesse Faden, Control
Laura Bailey as Kait Diaz, Gears 5
Mads Mikkelsen as Cliff, Death Stranding
Matthew Porretta as Dr. Casper Darling, Control
Norman Reedus as Sam Porter Bridges, Death Stranding
Best Action Game
Apex Legends
Astral Chain
Call of Duty: Modern Warfare
Devil May Cry 5
Gears 5
Metro Exodus
Best Action/Adventure Game
Borderlands 3
Control
Death Stranding
Resident Evil 2
The Legend of Zelda: Link’s Awakening
Sekiro: Shadows Die Twice
Best RPG
Disco Elysium
Final Fantasy XIV
Kingdom Hearts III
Monster Hunter World: Iceborne
The Outer Worlds
Best Fighting Game
Dead or Alive 6
Jump Force
Mortal Kombat 11
Samurai Showdown
Super Smash Bros. Ultimate
Best Family Game
Luigi’s Mansion 3
Ring Fit Adventure
Super Mario Maker 2
Super Smash Bros. Ultimate
Yoshi’s Crafted World
Best Strategy Game
Age of Wonders: Planetfall
Anno 1800
Fire Emblem: Three Houses
Total War: Three Kingdoms
Tropico 6
Wargroove
Best Sports/Racing Game
Crash Team Racing Nitro-Fueled
DiRT Rally 2.0
eFootball Pro Evolution Soccer 2020
F1 2019
FIFA 20
Best Multiplayer Game
Apex Legends
Borderlands 3
Call of Duty: Modern Warfare
Tetris 99
Tom Clancy’s The Division 2
Fresh Indie Game Studio
ZA/UM for Disco Elysium
Nomada Studio for Gris
DeadToast Entertainment for My Friend Pedro
Mobius Digital for Outer Wilds
Mega Crit for Slay the Spire
House House for Untitled Goose Game
Games for Impact
Concrete Genie
Gris
Kind Words
Life is Strange 2
Sea of Solitude
Best Ongoing Game
Apex Legends
Destiny 2
Final Fantasy XIV
Fortnite
Tom Clancy’s Rainbow Six Siege
Best Independent Game
Baba Is You
Disco Elysium
Katana ZERO
Outer Wilds
Untitled Goose Game
Best Mobile Game
Call of Duty: Mobile
GRINDSTONE
Sayonara Wild Hearts
Sky: Children of Light
What the Golf?
Best Community Support
Apex Legends
Destiny 2
Final Fantasy XIV
Fortnite
Tom Clancy’s Rainbow Six Siege
Best VR/AR Game
Asgard’s Wrath
Blood & Truth
Beat Saber
No Man’s Sky
Trover Saves the Universe
We’ll see how the awards shake out in under a month, though we’re (sadly) going to get a lot less representation from Western games this time around.


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DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Findings published today show families test-driving new special educational needs and disabilities system feel more supported than ever.
New findings published today show families test-driving the government’s new special educational needs and disabilities ( SEND ) system feel more supported than ever.
An independent report into the new support, available from this September, shows that the measures are already making a real difference to the lives of children, young people and families. The biggest transformation to SEND support for 30 years will give children and parents greater say over their personalised care and assistance, and put in place a new, continual birth-to-25 system for children and young people with SEND .
The news comes as SEND support enters the digital age with the launch of a brand new app that will give children and young people a voice in sharing important information about their needs in a simple, single place.
The Council for Disabled Children’s Early Support app will enable children and young people with special educational needs to upload details about their condition and the support they are receiving to share with people closely involved in their life, such as teachers and doctors. In turn, it will make it easier for those working with children and young people with SEND to understand their needs in an easy and straightforward way.
In using the app - and sharing their likes and dislikes and hopes for the future - young people can make sure the help they get is tailored to their needs and their voice heard in important decisions made about their lives. Strict safeguards will also be put in place to ensure their safety and privacy.
Children and Families Minister, Edward Timpson said:
It’s great news that families who’ve tested our reforms ahead of September are now saying they have more control over how and where they access support for their children.
For far too long, too many families have had to fight for the support they need. The new system will put children and young people first.
It’s vital that children and young people with SEND can have their say on important decisions affecting their future. The pioneering early support app will give young people a simple, single place to set out their needs to those who work with them - creating a digital link between the young person and their key professionals.
Findings from the pilots of the measures - being test-driven by families across 31 councils - also show that professionals are more supportive of the system, saying it helps them to work more effectively together and places families at the centre of the system. Professionals are also able to provide more tailored support to families, giving them the help and assistance they need.
A special online guide for parents has also been published today on www.gov.uk which clearly explains what the new changes will mean for their child, how the new system will work, and what help they should expect from their local education, health and social services.
From this September, a new package of ambitious changes transforming the support on offer will come into force. The new system will:
These new changes will be phased in from September 2014 - ensuring all children and young people with SEND and their families can benefit from the improved arrangements as soon as possible, while at the same time ensuring that the best possible service is maintained during the transition.
Around 2,500 families have now received the new Education, Health and Care ( EHC ) Plans as part of the pilot pathfinder programme, ensuring that support for children with special educational needs does not simply stop when they turn 18. The new reforms also mean that support can continue up until the age of 25 for those with an EHC plan who need more time to complete their further education or training.
Read the ‘ SEND pathways programme evaluation’ pathfinder reports . Findings about parental satisfaction are based on qualitative research conducted with 31 families between January 2014 and early March 2014 who participated in the new Education, Health and Care ( EHC ) planning pathway and as a result, received an EHC plan.
Read the ‘ SEND : guide for parents and carers’ ’.
The Council for disabled children ‘Early support app’ is being trialled with tablet users.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 15 August 2014


Price
Down Payment Percent
Down Payment Amount
Interest Rate
Term (in years)
Loan Amount: {{loan_amount() | currency }} Monthly Payment: {{ monthly_payment() | currency }}


fun formatCardNumber(cardNumber: String): String
Format a given card number to a neat string
cardNumber - The card number to format
cardNumber
Return The formatted credit card number properly spaced


You can use Google or geocode.io for geocoding by setting the following environment variables. Else, this defaults to using US Census geocode API.
To use Google geocoding set the environment variable:
GOOGLE_API_KEY=...To use geocod.io set the environment variable:
GEOCIDEIO_API_KEY=...For here.com set:
HERE_API_KEY=To geocode, e.g., with the following file: city,state Boulder,CO Laramie,WY Buffalo,NY Run: seesv:seesv -geocode city,state "" "" ... This concatenates the city and state field, e.g., "Boulder CO" and calls the API to geolocate.
To use internal location tables set the prefix argument to one of:
seesv -geocode columns "county:" "" ...
seesv -geocode columns "state:" "" ...
seesv -geocode columns "zip:" "" ...
seesv -geocode columns "country:" "" ...


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
Dietary
You’ll have lots of biscotti left over which can be stored in an airtight tub for up to a month and served with coffee or other creamy desserts like possets.
For this recipe you will need 4 large ramekins.
Preheat the oven to 180C/160C Fan/Gas 4.
For the crème caramels, put 175g/6oz sugar and 3 tablespoons water in a pan and bring to the boil. Cook for 4-5 minutes, or until the sugar is dissolved and turns a caramel colour. (CAUTION: boiling sugar is very hot.) Carefully pour a little of the caramel into the bottom of 4 large ramekins or cups, carefully swirling the caramel up the sides. Set aside to cool and harden.
Put the cream and vanilla pod in the pan that the caramel was made in and bring to a simmer.
Place the eggs and remaining 50g/1¾oz sugar in a bowl and whisk together until smooth, then pour the hot cream over, whisking all the time. Pass through a sieve and pour into the caramel-lined ramekins.
Place the ramekins in a deep roasting tin and pour enough hot water into the roasting tin to come two-thirds of the way up the sides of the ramekins. Bake for 15-20 minutes, or until just set but still have a bit of wobble (the exact time will depend on the size of the ramekins). Remove from the roasting tin and allow to cool completely before placing in the fridge to chill for 1-2 hours.
For the biscotti, preheat the oven to 180C/160C Fan/Gas 4 and line a baking tray with baking parchment.
In a large bowl, mix the flour, sugar and baking powder. Add half the beaten eggs and mix well, then add half of what's left and mix again. Stir in the remaining eggs a little at a time until the dough takes shape but isn't too wet (you may not need all the eggs.) Work in the fruit, nuts and lemon zest until well combined.
Divide the dough equally into six pieces. Wet your hands and roll each piece into a sausage shape about 5cm/2in wide and place well apart on the baking tray.
Lightly flatten the 'sausages' and bake for about 20 minutes, or until golden-brown. Remove from the oven and leave to cool and harden for 10 minutes.
Using a serrated knife, cut the 'sausages' on an angle into very thin slices and lay them cut-side down on the baking tray. (You may need another baking tray, or to do the next step in batches.)
Return the biscuits to the oven and bake for 8 minutes, then turn the slices over and cook for a further 8-10 minutes, or until they are a pale golden colour. Remove from the oven and cool on wire racks. When completely cold, the biscotti can be stored in airtight jars for a week.
To serve, turn the crème caramels out onto serving plates and place the biscotti alongside.


Hi, my name is Hung, a junior developer who believe in "Coding for Good". Currently, I have been learning with FreeCodeCamp and joining some courses regarding Front End Development in Udemy. My goal is to achieve my Front End Developer Certification at the end of this year and start my career as FED soon
You may contact me using Facebook, Github, Linkedin or Twitter, just click on the icons below:
I believe that this personal site is simply a beginning. I commit to work hard so I can update it regularly, hope that you may find some thing useful.
Feel free to give me any feedback on the content, suggestion on anything can be improved. I'm glad to hear from you all. Thanks in advance
H012.us 2016. All Rights Reserved


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$199
<1,500 square foot of attached interior space plus covered lanai's and attached garages at no additional cost.
$249
1,500-3,000 square foot of attached space plus covered lanai's and attached garages at no additional cost.
$299
3,001-5,000 square foot of attached space plus covered lanai's and attached garages at no additional cost.
$.08/foot
5,001+ square foot of attached space plus covered lanai's and attached garages at no additional cost.
*We offer great value pricing compared to our competitors, since we include extras others charge you for...
Walk thru slideshow created with every 3D tour at no charge to you.
2D floor plan included with every 3D tour. Some companies charge as much as $50-$100 for this.
Core VR included with every 3D tour. Some companies charge as much as $20 for Core VR.
1 year 3D Tour hosting inlcuded with every 3D tour. Some companies charge as much as $50 for initial 3D hosting.
360 detached scans are included of the front and the back of property and added to the slideshow. Some companies charge extra for 360 detached scans.
Branded and non branded 3D tours are included with every 3D tour.
*We offer several add-on's to our 3D tour packages...
High Quality processed HDR(high dynamic range photography) taken by a separate DSLR professional camera for much better quality photos than the extracted photos from the 3D tour that some competitors offer.
Mattertags can be added to note specific features within the property. Mattertags can also be added to link to videos, PDF's, photos, web pages and even additional 3d tour(s) and more.
roOomy, a leading virtual staging and 3D modeling platform, announced its collaboration with Matterport, an immersive 3D media technology company, to develop first-to-market solutions that integrate its virtual staging offering with Matterport’s immersive 3D media platform. Currently under development in pilot phase with multiple partners, and expected to roll out later in 2018, roOomy’s virtual staging will allow Matterport customers to virtually furnish spaces including apartments or homes, providing property managers or real estate agents the ability to cost effectively display units in a furnished state, and enabling renters and buyers to make more informed, confident decisions during their search for a residence.
To experience two roOomy virtually staged Matterport 3D Tours in action, click on below.


CSS4J
CSS4J provides a CSS object model API for the Java™ platform, closely related to the CSSOM and DOM APIs.
Documentation
- CSS4J basic usage.
- Embedding SVG in HTML documents.
- XML parsing in Java with
DefaultEntityResolver
. - The F.A.Q.
- Latest modular javadocs.
Old javadocs:
Licensing
This software is released under a BSD-style license, although some individual files may have
a different one (see NOTICE.txt
in the distribution).
Downloads
Core module
Agent module
AWT module
DOM4J module
EchoSVG
EchoSVG is a SVG implementation (fork of Batik) that uses the css4j NSAC parser.
Direct installation to local Maven repository
The CSS4J artifacts (together with a few dependencies not available from Maven Central like JCLF or a recent XMLPull-XPP3) can be installed directly to your local Maven repository by using these scripts:
install-jclf.sh
(5.0).install-xpp3.sh
(1.2, optional dependency of css4j-dom4j).install-css4j.sh
(3.x).
The scripts execute Maven plugins: if you run them from a directory which has a pom.xml
file, you may experience problems or delays caused by Maven processing that file.
More information about using the library in a Gradle or Maven project is given at the usage guide.
Community - Support
There is a general forum that can be used for css4j support, and the development forum is intended for discussions about patches and APIs.
- Css4j general forum (Google Groups).
- Css4j development forum (Google Groups). If you have patches to contribute, either use Github or send them to this group.


NetGear Arlo
Welcome,
NetGear Arlo
General
Home
Report
Help
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North america
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China
Asia Pacific
Camera Wise Report
Recent Activities


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Stay positive and happy. Work hard and don't give up hope. Be open to criticism and keep learning. Surround yourself with happy, warm and genuine people.
About me:
Originaly I am from Ukraine.
Recently I have decided to become a developer, so right now I am moving in that direction.
I am taking classes to learn how to program.
I am very energetic, fun person to be arround. If you want to chat over a cup of coffee let me know.
Interests:
Currently I am a student in the Epicodus bootcamp.
In the nearest future I will become a software developer.
Languahes used: HTML, CSS.
Dtails: project was made to create the website shows the portfolio page.
Languahes used: HTML, CSS.
Dtails: the website is recreating th Wikipedia home page.
Languahes used: HTML, CSS.
Dtails: the website of a vacation spot in Indonesia.
Languahes used: HTML, CSS.
Dtails: the website for a band.


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