Operator Changelog¶
This file is automatically generated from the def files via this script. Do not modify directly and instead edit operator definitions.
For an operator input/output’s differentiability, it can be differentiable, non-differentiable, or undefined. If a variable’s differentiability is not specified, that variable has undefined differentiability.
ai.onnx.ml¶
Version 1 of the ‘ai.onnx.ml’ operator set¶
ai.onnx.ml.ArrayFeatureExtractor-1¶
Select elements of the input tensor based on the indices passed.
The indices are applied to the last axes of the tensor.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Inputs¶
- X : T
- Data to be selected
- Y : tensor(int64)
- The indices, based on 0 as the first index of any dimension.
Outputs¶
- Z : T
- Selected output data as an array
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32), tensor(string)
- The input must be a tensor of a numeric type or string. The output will be of the same tensor type.
ai.onnx.ml.Binarizer-1¶
Maps the values of the input tensor to either 0 or 1, element-wise, based on the outcome of a comparison against a threshold value.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- threshold : float (default is 0.0)
- Values greater than this are mapped to 1, others to 0.
Inputs¶
- X : T
- Data to be binarized
Outputs¶
- Y : T
- Binarized output data
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type. The output will be of the same tensor type.
ai.onnx.ml.CastMap-1¶
Converts a map to a tensor.
The map key must be an int64 and the values will be ordered
in ascending order based on this key.
The operator supports dense packing or sparse packing.
If using sparse packing, the key cannot exceed the max_map-1 value.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- cast_to : string (default is TO_FLOAT)
- A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'.
- map_form : string (default is DENSE)
- Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).
One of 'DENSE', 'SPARSE'. - max_map : int (default is 1)
- If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor.
Inputs¶
- X : T1
- The input map that is to be cast to a tensor
Outputs¶
- Y : T2
- A tensor representing the same data as the input map, ordered by their keys
Type Constraints¶
- T1 : map(int64, string), map(int64, float)
- The input must be an integer map to either string or float.
- T2 : tensor(string), tensor(float), tensor(int64)
- The output is a 1-D tensor of string, float, or integer.
ai.onnx.ml.CategoryMapper-1¶
Converts strings to integers and vice versa.
Two sequences of equal length are used to map between integers and strings,
with strings and integers at the same index detailing the mapping.
Each operator converts either integers to strings or strings to integers, depending
on which default value attribute is provided. Only one default value attribute
should be defined.
If the string default value is set, it will convert integers to strings.
If the int default value is set, it will convert strings to integers.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- cats_int64s : list of ints
- The integers of the map. This sequence must be the same length as the 'cats_strings' sequence.
- cats_strings : list of strings
- The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence
- default_int64 : int (default is -1)
- An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined. - default_string : string (default is _Unused)
- A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
Inputs¶
- X : T1
- Input data
Outputs¶
- Y : T2
- Output data. If strings are input, the output values are integers, and vice versa.
Type Constraints¶
- T1 : tensor(string), tensor(int64)
- The input must be a tensor of strings or integers, either [N,C] or [C].
- T2 : tensor(string), tensor(int64)
- The output is a tensor of strings or integers. Its shape will be the same as the input shape.
ai.onnx.ml.DictVectorizer-1¶
Uses an index mapping to convert a dictionary to an array.
Given a dictionary, each key is looked up in the vocabulary attribute corresponding to
the key type. The index into the vocabulary array at which the key is found is then
used to index the output 1-D tensor ‘Y’ and insert into it the value found in the dictionary ‘X’.
The key type of the input map must correspond to the element type of the defined vocabulary attribute.
Therefore, the output array will be equal in length to the index mapping vector parameter.
All keys in the input dictionary must be present in the index mapping vector.
For each item in the input dictionary, insert its value in the output array.
Any keys not present in the input dictionary, will be zero in the output array.
For example: if the string_vocabulary
parameter is set to ["a", "c", "b", "z"]
,
then an input of {"a": 4, "c": 8}
will produce an output of [4, 8, 0, 0]
.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- int64_vocabulary : list of ints
- An integer vocabulary array.
One and only one of the vocabularies must be defined. - string_vocabulary : list of strings
- A string vocabulary array.
One and only one of the vocabularies must be defined.
Inputs¶
- X : T1
- A dictionary.
Outputs¶
- Y : T2
- A 1-D tensor holding values from the input dictionary.
Type Constraints¶
- T1 : map(string, int64), map(int64, string), map(int64, float), map(int64, double), map(string, float), map(string, double)
- The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same.
- T2 : tensor(int64), tensor(float), tensor(double), tensor(string)
- The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary.
ai.onnx.ml.FeatureVectorizer-1¶
Concatenates input tensors into one continuous output.
All input shapes are 2-D and are concatenated along the second dimention. 1-D tensors are treated as [1,C].
Inputs are copied to the output maintaining the order of the input arguments.
All inputs must be integers or floats, while the output will be all floating point values.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- inputdimensions : list of ints
- The size of each input in the input list
Inputs (1 - ∞)¶
- X (variadic) : T1
- An ordered collection of tensors, all with the same element type.
Outputs¶
- Y : tensor(float)
- The output array, elements ordered as the inputs.
Type Constraints¶
- T1 : tensor(int32), tensor(int64), tensor(float), tensor(double)
- The input type must be a tensor of a numeric type.
ai.onnx.ml.Imputer-1¶
Replaces inputs that equal one value with another, leaving all other elements alone.
This operator is typically used to replace missing values in situations where they have a canonical
representation, such as -1, 0, NaN, or some extreme value.
One and only one of imputed_value_floats or imputed_value_int64s should be defined – floats if the input tensor
holds floats, integers if the input tensor holds integers. The imputed values must all fit within the
width of the tensor element type. One and only one of the replaced_value_float or replaced_value_int64 should be defined,
which one depends on whether floats or integers are being processed.
The imputed_value attribute length can be 1 element, or it can have one element per input feature.
In other words, if the input tensor has the shape [*,F], then the length of the attribute array may be 1 or F. If it is 1, then it is broadcast along the last dimension and applied to each feature.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- imputed_value_floats : list of floats
- Value(s) to change to
- imputed_value_int64s : list of ints
- Value(s) to change to.
- replaced_value_float : float (default is 0.0)
- A value that needs replacing.
- replaced_value_int64 : int (default is 0)
- A value that needs replacing.
Inputs¶
- X : T
- Data to be processed.
Outputs¶
- Y : T
- Imputed output data
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape.
ai.onnx.ml.LabelEncoder-1¶
Converts strings to integers and vice versa.
If the string default value is set, it will convert integers to strings.
If the int default value is set, it will convert strings to integers.
Each operator converts either integers to strings or strings to integers, depending
on which default value attribute is provided. Only one default value attribute
should be defined.
When converting from integers to strings, the string is fetched from the
‘classes_strings’ list, by simple indexing.
When converting from strings to integers, the string is looked up in the list
and the index at which it is found is used as the converted value.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- classes_strings : list of strings
- A list of labels.
- default_int64 : int (default is -1)
- An integer to use when an input string value is not found in the map.
One and only one of the 'default_*' attributes must be defined. - default_string : string (default is _Unused)
- A string to use when an input integer value is not found in the map.
One and only one of the 'default_*' attributes must be defined.
Inputs¶
- X : T1
- Input data.
Outputs¶
- Y : T2
- Output data. If strings are input, the output values are integers, and vice versa.
Type Constraints¶
- T1 : tensor(string), tensor(int64)
- The input type must be a tensor of integers or strings, of any shape.
- T2 : tensor(string), tensor(int64)
- The output type will be a tensor of strings or integers, and will have the same shape as the input.
ai.onnx.ml.LinearClassifier-1¶
Linear classifier
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- classlabels_ints : list of ints
- Class labels when using integer labels. One and only one 'classlabels' attribute must be defined.
- classlabels_strings : list of strings
- Class labels when using string labels. One and only one 'classlabels' attribute must be defined.
- coefficients : list of floats (required)
- A collection of weights of the model(s).
- intercepts : list of floats
- A collection of intercepts.
- multi_class : int (default is 0)
- Indicates whether to do OvR or multinomial (0=OvR is the default).
- post_transform : string (default is NONE)
- Indicates the transform to apply to the scores vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT'
Inputs¶
- X : T1
- Data to be classified.
Outputs¶
- Y : T2
- Classification outputs (one class per example).
- Z : tensor(float)
- Classification scores ([N,E] - one score for each class and example
Type Constraints¶
- T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C]
- T2 : tensor(string), tensor(int64)
- The output will be a tensor of strings or integers.
ai.onnx.ml.LinearRegressor-1¶
Generalized linear regression evaluation.
If targets is set to 1 (default) then univariate regression is performed.
If targets is set to M then M sets of coefficients must be passed in as a sequence
and M results will be output for each input n in N.
The coefficients array is of length n, and the coefficients for each target are contiguous.
Intercepts are optional but if provided must match the number of targets.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- coefficients : list of floats
- Weights of the model(s).
- intercepts : list of floats
- Weights of the intercepts, if used.
- post_transform : string (default is NONE)
- Indicates the transform to apply to the regression output vector.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' - targets : int (default is 1)
- The total number of regression targets, 1 if not defined.
Inputs¶
- X : T
- Data to be regressed.
Outputs¶
- Y : tensor(float)
- Regression outputs (one per target, per example).
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type.
ai.onnx.ml.Normalizer-1¶
Normalize the input. There are three normalization modes, which have the corresponding formulas,
defined using element-wise infix operators ‘/’ and ‘^’ and tensor-wide functions ‘max’ and ‘sum’:
Max: Y = X / max(X)
L1: Y = X / sum(X)
L2: Y = sqrt(X^2 / sum(X^2)}
In all modes, if the divisor is zero, Y == X.
For batches, that is, [N,C] tensors, normalization is done along the C axis. In other words, each row
of the batch is normalized independently.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- norm : string (default is MAX)
- One of 'MAX,' 'L1,' 'L2'
Inputs¶
- X : T
- Data to be encoded, a tensor of shape [N,C] or [C]
Outputs¶
- Y : tensor(float)
- Encoded output data
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type.
ai.onnx.ml.OneHotEncoder-1¶
Replace each input element with an array of ones and zeros, where a single
one is placed at the index of the category that was passed in. The total category count
will determine the size of the extra dimension of the output array Y.
For example, if we pass a tensor with a single value of 4, and a category count of 8,
the output will be a tensor with [0,0,0,0,1,0,0,0]
.
This operator assumes every input feature is from the same set of categories.
If the input is a tensor of float, int32, or double, the data will be cast
to integers and the cats_int64s category list will be used for the lookups.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- cats_int64s : list of ints
- List of categories, ints.
One and only one of the 'cats_*' attributes must be defined. - cats_strings : list of strings
- List of categories, strings.
One and only one of the 'cats_*' attributes must be defined. - zeros : int (default is 1)
- If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail.
Inputs¶
- X : T
- Data to be encoded.
Outputs¶
- Y : tensor(float)
- Encoded output data, having one more dimension than X.
Type Constraints¶
- T : tensor(string), tensor(int64), tensor(int32), tensor(float), tensor(double)
- The input must be a tensor of a numeric type.
ai.onnx.ml.SVMClassifier-1¶
Support Vector Machine classifier
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- classlabels_ints : list of ints
- Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. - classlabels_strings : list of strings
- Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. - coefficients : list of floats
- kernel_params : list of floats
- List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
- kernel_type : string (default is LINEAR)
- The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
- post_transform : string (default is NONE)
- Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' - prob_a : list of floats
- First set of probability coefficients.
- prob_b : list of floats
- Second set of probability coefficients. This array must be same size as prob_a.
If these are provided then output Z are probability estimates, otherwise they are raw scores. - rho : list of floats
- support_vectors : list of floats
- vectors_per_class : list of ints
Inputs¶
- X : T1
- Data to be classified.
Outputs¶
- Y : T2
- Classification outputs (one class per example).
- Z : tensor(float)
- Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores.
Type Constraints¶
- T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type, either [C] or [N,C].
- T2 : tensor(string), tensor(int64)
- The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input.
ai.onnx.ml.SVMRegressor-1¶
Support Vector Machine regression prediction and one-class SVM anomaly detection.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- coefficients : list of floats
- Support vector coefficients.
- kernel_params : list of floats
- List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel.
- kernel_type : string (default is LINEAR)
- The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'.
- n_supports : int (default is 0)
- The number of support vectors.
- one_class : int (default is 0)
- Flag indicating whether the regression is a one-class SVM or not.
- post_transform : string (default is NONE)
- Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' - rho : list of floats
- support_vectors : list of floats
- Chosen support vectors
Inputs¶
- X : T
- Data to be regressed.
Outputs¶
- Y : tensor(float)
- Regression outputs (one score per target per example).
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input type must be a tensor of a numeric type, either [C] or [N,C].
ai.onnx.ml.Scaler-1¶
Rescale input data, for example to standardize features by removing the mean and scaling to unit variance.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- offset : list of floats
- First, offset by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count. - scale : list of floats
- Second, multiply by this.
Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.
Must be same length as 'offset'
Inputs¶
- X : T
- Data to be scaled.
Outputs¶
- Y : tensor(float)
- Scaled output data.
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input must be a tensor of a numeric type.
ai.onnx.ml.TreeEnsembleClassifier-1¶
Tree Ensemble classifier. Returns the top class for each of N inputs.
The attributes named ‘nodes_X’ form a sequence of tuples, associated by
index into the sequences, which must all be of equal length. These tuples
define the nodes.
Similarly, all fields prefixed with ‘class_’ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by
the associated class_weights index.
One and only one of classlabels_strings or classlabels_int64s
will be defined. The class_ids are indices into this list.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- base_values : list of floats
- Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
- class_ids : list of ints
- The index of the class list that each weight is for.
- class_nodeids : list of ints
- node id that this weight is for.
- class_treeids : list of ints
- The id of the tree that this node is in.
- class_weights : list of floats
- The weight for the class in class_id.
- classlabels_int64s : list of ints
- Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. - classlabels_strings : list of strings
- Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. - nodes_falsenodeids : list of ints
- Child node if expression is false.
- nodes_featureids : list of ints
- Feature id for each node.
- nodes_hitrates : list of floats
- Popularity of each node, used for performance and may be omitted.
- nodes_missing_value_tracks_true : list of ints
- For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes. - nodes_modes : list of strings
- The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' - nodes_nodeids : list of ints
- Node id for each node. Ids may restart at zero for each tree, but it not required to.
- nodes_treeids : list of ints
- Tree id for each node.
- nodes_truenodeids : list of ints
- Child node if expression is true.
- nodes_values : list of floats
- Thresholds to do the splitting on for each node.
- post_transform : string (default is NONE)
- Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.'
Inputs¶
- X : T1
- Input of shape [N,F]
Outputs¶
- Y : T2
- N, Top class for each point
- Z : tensor(float)
- The class score for each class, for each point, a tensor of shape [N,E].
Type Constraints¶
- T1 : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input type must be a tensor of a numeric type.
- T2 : tensor(string), tensor(int64)
- The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used.
ai.onnx.ml.TreeEnsembleRegressor-1¶
Tree Ensemble regressor. Returns the regressed values for each input in N.
All args with nodes_ are fields of a tuple of tree nodes, and
it is assumed they are the same length, and an index i will decode the
tuple across these inputs. Each node id can appear only once
for each tree id.
All fields prefixed with target_ are tuples of votes at the leaves.
A leaf may have multiple votes, where each vote is weighted by
the associated target_weights index.
All trees must have their node ids start at 0 and increment by 1.
Mode enum is BRANCH_LEQ, BRANCH_LT, BRANCH_GTE, BRANCH_GT, BRANCH_EQ, BRANCH_NEQ, LEAF
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- aggregate_function : string (default is SUM)
- Defines how to aggregate leaf values within a target.
One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' - base_values : list of floats
- Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0)
- n_targets : int
- The total number of targets.
- nodes_falsenodeids : list of ints
- Child node if expression is false
- nodes_featureids : list of ints
- Feature id for each node.
- nodes_hitrates : list of floats
- Popularity of each node, used for performance and may be omitted.
- nodes_missing_value_tracks_true : list of ints
- For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.
This attribute may be left undefined and the defalt value is false (0) for all nodes. - nodes_modes : list of strings
- The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' - nodes_nodeids : list of ints
- Node id for each node. Node ids must restart at zero for each tree and increase sequentially.
- nodes_treeids : list of ints
- Tree id for each node.
- nodes_truenodeids : list of ints
- Child node if expression is true
- nodes_values : list of floats
- Thresholds to do the splitting on for each node.
- post_transform : string (default is NONE)
- Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' - target_ids : list of ints
- The index of the target that each weight is for
- target_nodeids : list of ints
- The node id of each weight
- target_treeids : list of ints
- The id of the tree that each node is in.
- target_weights : list of floats
- The weight for each target
Inputs¶
- X : T
- Input of shape [N,F]
Outputs¶
- Y : tensor(float)
- N classes
Type Constraints¶
- T : tensor(float), tensor(double), tensor(int64), tensor(int32)
- The input type must be a tensor of a numeric type.
ai.onnx.ml.ZipMap-1¶
Creates a map from the input and the attributes.
The values are provided by the input tensor, while the keys are specified by the attributes.
Must provide keys in either classlabels_strings or classlabels_int64s (but not both).
The columns of the tensor correspond one-by-one to the keys specified by the attributes. There must be as many columns as keys.
Version¶
This version of the operator has been available since version 1 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- classlabels_int64s : list of ints
- The keys when using int keys.
One and only one of the 'classlabels_*' attributes must be defined. - classlabels_strings : list of strings
- The keys when using string keys.
One and only one of the 'classlabels_*' attributes must be defined.
Inputs¶
- X : tensor(float)
- The input values
Outputs¶
- Z : T
- The output map
Type Constraints¶
- T : seq(map(string, float)), seq(map(int64, float))
- The output will be a sequence of string or integer maps to float.
Version 2 of the ‘ai.onnx.ml’ operator set¶
ai.onnx.ml.LabelEncoder-2¶
Maps each element in the input tensor to another value.
The mapping is determined by the two parallel attributes, ‘keys_’ and
‘values_’ attribute. The i-th value in the specified ‘keys_’ attribute
would be mapped to the i-th value in the specified ‘values_’ attribute. It
implies that input’s element type and the element type of the specified
‘keys_’ should be identical while the output type is identical to the
specified ‘values_’ attribute. If an input element can not be found in the
specified ‘keys_’ attribute, the ‘default_’ that matches the specified
‘values_’ attribute may be used as its output value.
Let’s consider an example which maps a string tensor to an integer tensor.
Assume and ‘keys_strings’ is [“Amy”, “Sally”], ‘values_int64s’ is [5, 6],
and ‘default_int64’ is ‘-1’. The input [“Dori”, “Amy”, “Amy”, “Sally”,
“Sally”] would be mapped to [-1, 5, 5, 6, 6].
Since this operator is an one-to-one mapping, its input and output shapes
are the same. Notice that only one of ‘keys_’/’values_’ can be set.
For key look-up, bit-wise comparison is used so even a float NaN can be
mapped to a value in ‘values_’ attribute.
Version¶
This version of the operator has been available since version 2 of the ‘ai.onnx.ml’ operator set.
Attributes¶
- default_float : float (default is -0.0)
- A float.
- default_int64 : int (default is -1)
- An integer.
- default_string : string (default is _Unused)
- A string.
- keys_floats : list of floats
- A list of floats.
- keys_int64s : list of ints
- A list of ints.
- keys_strings : list of strings
- A list of strings. One and only one of 'keys_*'s should be set.
- values_floats : list of floats
- A list of floats.
- values_int64s : list of ints
- A list of ints.
- values_strings : list of strings
- A list of strings. One and only one of 'value_*'s should be set.
Inputs¶
- X : T1
- Input data. It can be either tensor or scalar.
Outputs¶
- Y : T2
- Output data.
Type Constraints¶
- T1 : tensor(string), tensor(int64), tensor(float)
- The input type is a tensor of any shape.
- T2 : tensor(string), tensor(int64), tensor(float)
- Output type is determined by the specified 'values_*' attribute.


Mushrooms - P. azurescens, Fluoxetine (Prozac) & Alcohol
I have enjoyed mushrooms for years, but I just started taking Prozac (one 20mg pill daily) six weeks ago and I was wondering if this would effect my experience. I am happy to report that I had a lovely time.
My friends and I made tea out of some dried Psilocybe azurescens we had picked the week before. This took place at a big weekend campout/party. I had already decided to skip my daily dose of Prozac (just for one day) because I wanted to be able to drink beer around the campfire and I have noticed that Prozac causes alcohol to make me rather sleepy and a bit nauseous. I had probably had about 5 beers over the course of 3 hours when I ingested 1 cup of mushroom tea.
It only took about 10 minutes until I could feel it coming on, and within half an hour I was starting to peak. For a solid hour I laid with my back on the earth, looking up into an intensely starry sky, experiencing the usual sense of rich deep beauty, goodness, connectedness, belonging, and appreciation that P.azurescens has always shared with me. For hours afterwards I could still feel them. The whole “trip” lasted probably around 5 hours, which seems about usual. The only thing that was at all different was that the next day I felt a little over-emotional and weepy, but that could have been due to my skipping the pill, my alcohol consumption, or any number of other factors.
In short, I just wanted to let anyone know who might be worried about mixing Prozac and Psilocybin that I had no problem with it. Every time I take P. azurescens I feel that I am taking good medicine. As for Prozac and alcohol, that combo I’m afraid is not so good for me, but then alcohol never felt like very good medicine to begin with.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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The DBC experience is not just an opportunity to learn how to program, its the chance to grow as a person, not only in the programming world, but in the real world. We are learning to learn for the rest of our lives. We are here to learn code yes, and that entails continuing the programming education even after we land jobs, but as an individual, one should strive for life-long learning. Approaching things we don't understand, just so we can tackle it and see just how much we can learn from something, is one of the best ways to build your knowledge.
I believe that one of the key aspects of the DBC experience is attacking issues head on. This is an approach that can be adapted in many facets of our non-programming lives, and that is one of the goals of DBC: to make us better learners and better people overall. My impression of DBC is that this will be a place where the teachers are just as enthusiastic about learning as we are. I believe every person will have something to contribute to every one else's DBC experience, especially since we will be struggling through this all together. Despite not feeling like the smartest person in the room, learning from failure is still a viable option for creating a path to success.
The culture of learning is what drives DBC and I intend on engaging in this by any means necessary whether it be engaging in offic hours, choosing to get to spend time with my cohort members outside of class, taking the time to rework challenges that I hadn't done so well on intially, etc. Learning how to learn is a task in itself, but once that is done, continuing to feed the drive for more knowledge becomes second nature. Going that extra distance for understanding or reaching out for help is something that I am more than willing to do because I know that this is important to me and the people that are learning from me in return.
My expectations of DBC haven't really changed much. I expect people to struggle sometimes, and for people to have "aha" moments. I realize that its more of a community now so that makes me feel a little more comefortable, however I am still a litte nervous about falling behind, but if it happens, it will be a learning opportunity so bring it on! I am beyond excited to experience this kind of learning environment, especially since I know there will be lots of help when I am working on improving myself.
| T-1 | T-2 | T-3 | T-4 | T-5 | T-6 | T-7 | T-8 |
| C-1 | C-3 | C-4 | C-5 | C-6 | C-7 | C-8 | C-9 |


Object.finalize()
method that are
declared public
.
According to the contract of the Object.finalize()
, only the garbage
collector calls this method. Making this method public may be confusing, because it
means that the method can be used from other code.
A quick-fix is provided to make the method protected
, to prevent it from being invoked
from other classes.
Example:
class X {
public void finalize() {
/* ... */
}
}
After the quick-fix is applied:
class X {
protected void finalize() {
/* ... */
}
}


The highs and lows of Jose Mourinho
by BeSoccerA brief history of the career of Jose Mourinho, 56, who was named as Tottenham Hotspur manager on Wednesday:
- The translator -
After an undistinguished playing career in Portugal and a spell as a PE teacher, Mourinho worked as an interpreter for former England manager Bobby Robson at Portugal's Sporting CP and then Porto, where they enjoyed huge success before moving to Barcelona in 1996. Robson lasted only one season at the helm of the Spanish giants but Mourinho remained as assistant to Dutchman Louis van Gaal, before securing the manager's role at Benfica in 2000.
- The European Cup-winner -
Short spells at Benfica and Leiria were followed by a move to Porto in early 2002, where he won the UEFA Cup, Portuguese league and Portuguese cup in his first full season, with future stars such as Deco, Ricardo Carvalho and Vitor Baia. An even greater success was to come the following year when Porto were the shock winners of European club football's biggest prize, the UEFA Champions League, beating Monaco 3-0 in the May 2004 final after eliminating Manchester United,Â LyonÂ andÂ Deportivo La Coruna along the way.
- The Special One -
Chelsea, bankrolled by Russian billionaire Roman Abramovich, immediately came knocking and Mourinho was unveiled to great fanfare in June 2004, memorably describing himself as "The Special One" to assembled media. It was not an idle boast: Chelsea won the next two editions of the Premier League, and added one FA Cup and two League Cup titles in a successful first stint at the London club. Mourinho's meticulous attention to detail, hitherto unknown in the English game, and attritional, pressing style cut a swathe through English football. But relations with Abramovich soured and Mourinho departed early in the 2007/08 season.
- The Italian job -
Featured links
- BREAKING NEWS: Mourinho replaces Pochettino at Tottenham
- The latest football transfers news and rumours
- Wales, golf, Madrid... in that order
- Luis Enrique returns to Spain national team
Mourinho joined Italy's Inter Milan for 2008/09 where his legend grew with two Scudettos in his first two seasons plus his second Champions League title in May 2010, beating Bayern Munich 2-0 in the final. At the time Mourinho, who clinched the Italian treble that year, was only the fourth manager to win the European title with two different clubs.Â
- The Real deal -
Despite his success Mourinho immediately departed for Real Madrid, a dream move for a manager, but his return to Spain proved ill-fated. Real won the Spanish Cup in Mourinho's first season in charge, but arch-rivals Barcelona beat them to the La Liga title and knocked them out in the Champions League semi-finals. The following year, Real won La Liga but again fell in the Champions League semi-finals. Mourinho's tenure unravelled in the third season as he feuded with players including Cristiano Ronaldo, the media and officials, and poked Barcelona assistant coach Tito Vilanova in the eye during a mass brawl. He departed at the end of the 2013 season.
- The second coming -
After being snubbed by Manchester United, who chose David Moyes to replace the retiring Alex Ferguson, Mourinho rekindled his affair with Chelsea in 2013. Chelsea won the Premier League title and the League Cup for a domestic double in 2014/15, but with enthusiasm dwindling for Mourinho's defence-minded football, passions cooled and he left in December 2015.
- The Manchester hotel -
Manchester United, struggling to find a viable replacement for Ferguson, turned to Mourinho in 2016 but the long-awaited match was not a happy one. Mourinho, lacking the charisma of earlier incarnations, and choosing to live full-time in a hotel, won the Europa League and League Cup in a promising first season. But as relations with the media deteriorated, and fans grew unhappy with United's style of play, Mourinho was unceremoniously sacked in December 2018.


World Water Bottle Flipping Championships
The most ridiculously awesome competition in the history of ever happened March 12, 2017 at Hackingtons in Rocklin.
Online Code School & Blog
The most ridiculously awesome competition in the history of ever happened March 12, 2017 at Hackingtons in Rocklin.
We know it is intimidating to try and help your student with their code if you have never coded before. If you see some red or pink on the coding page there are errors. These 5 tips will help you trouble shoot code even as a non-coder. It’s easier than you think!
Server Name: hackingtons-pixelmon.mcpro.co
Pixelmon is an advanced Minecraft multiplayer game that adds Pokemon characters to Minecraft.
Follow our guide to install the pixelmon mod prior to joining the server.
Join our basic Minecraft server and have fun building and avoiding creepers. Watch our tutorial for installing Minecraft if it is your first time.
Server #1: hackingtons1.mcpro.co
Server #2: hackingtons-standard.mcproco
Tips and tricks to keep yourself and your kids safe when browsing the web. We build websites, we know how they work and can show you what to avoid or what to proactively do to stay safe online.


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For any questions, feedback or suggestions:
nlpcraft
tag
By downloading NLPCraft you are agreeing to Apache 2.0 License terms & conditions.
Download the NLPCraft release - {{site.latest_version}}
is the latest version:
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|---|---|---|---|---|---|
| {% if rel.version == site.latest_version %} {% endif %} {{rel.zip_name}} {% unless rel.backward_compatible %}{% endunless %} {% if rel.version == site.latest_version %} new {% endif %} {% if rel.version == site.latest_version %} {% endif %} |
{% if rel.version == site.latest_version %} {% endif %}
|
{% if rel.version == site.latest_version %} {% endif %}
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{% if rel.version == site.latest_version %} {% endif %} {{rel.date}} {% if rel.version == site.latest_version %} {% endif %} | SHA256 PGP |
Release Verification
All release files should be verified using the PGP signatures and the project release KEYS. See official ASF verification instructions for a description of using the PGP and KEYS files for verification. A SHA512 checksum is also provided as an alternative verification method.
Download the NLPCraft binary release - {{site.latest_version}}
is the latest version:
| File Name | Version | Java - Scala | Release Date | Signatures | |
|---|---|---|---|---|---|
| {% if rel.version == site.latest_version %} {% endif %} {{rel.zip_name}} {% unless rel.backward_compatible %}{% endunless %} {% if rel.version == site.latest_version %} new {% endif %} {% if rel.version == site.latest_version %} {% endif %} | {% if rel.version == site.latest_version %} {% endif %} {{rel.version}} {% if rel.version == site.latest_version %} {% endif %} |
{% if rel.version == site.latest_version %} {% endif %}
|
SHA256 PGP | {% if rel.dockerhub_link %}{% endif %} |
Apache Release Policy
Binary downloads are provided for the convenience of our users and are not official Apache NLPCraft releases. In order to verify the release, we recommend that you download the official source distribution and verify the signatures of the downloaded files before opening them. See more information about Apache release policy.
To build binaries from the source release download the source release and run the following commands:
Unpack the source package:$ unzip -q apache-nlpcraft-incubating-{{site.latest_version}}.zip $ cd apache-nlpcraft-incubating-{{site.latest_version}}Build NLPCraft:
$ mvn clean package -P examples -DskipTests
Built JARs and Javadoc will be located in target
sub-folders of individual modules.
We highly recommend verifying the integrity and authenticity of the ZIP archive by comparing cryptographic signatures. Following instructions assume binary release on MacOS/Linux environment but the verification steps are similar for Windows.
If you want to contribute to the project you need to get the full developer snapshot from GitHub mirror:
$ git clone https://github.com/apache/incubator-nlpcraft.git $ mvn clean package -P examples
GitHub vs. Apache Release
Note that cloned GitHub repository is NOT an official Apache release and it does not come with the guarantees made by the official Apache release in terms of the licensing. GitHub repository may include content not licensed under ASL 2.0. Only the official Apache source release is guaranteed to be cleanly licensed under ASL 2.0.


| About Dailyrotten |
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Feedback
If you like this page, hate it, or would like to suggest changes,
<EMAIL_ADDRESS>is listening. This page
is experimental and somewhat different from what we usually offer, so please
do send your feedback.
Adding Stories
If you are a rotten newshound and you would like to suggest a story,
visit our submit page to
send us the URL. Stories that are accepted will have your name credited.
Posting Comments
In order to post comments to stories, you will have to
create an account.
Accounts will generally be validated within 24 hours, and of
course will be disabled if there is abuse.
Viewership
On the first full day this page went live, 100,000 people visited this Rotten
News page. Of course that was the day after the WTC attacks, so that number is
somewhat distorted. Right now this page gets between 50,000 and 75,000 people
every day.
Linking
We encourage everyone to link to this page; if you like it, others probably
will as well.
Other Rotten
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February 05, 2002
Teens who hanged dog evaporate
Edmonton Sun | Submitted by: FalseProphet
"A 10-year-old boy confessed to police last night that he lied about three
teens hanging his dog in front of him at a St. Albert playground on the
weekend. David Barney told police he tied his dog to the top of a slide,
then told his dog to stay while he went down. Sheba, the family dog, didn't
listen and tried to jump onto the slide - hanging herself in the process."
|
|
Read article...
|
|
Man scalps woman
BBC | Submitted by: Lumpy
"A man with a sexual fetish for hair has admitted scalping a woman in his
bedsit [studio apartment]. At the High Court in Perth, Leonard Bowie, 62,
pleaded guilty to assaulting Mary Mullady to her severe injury and permanent
disfigurement. The 51-year-old lost her long hair and most of the flesh on
top of her head in the attack in a terraced house in Devonshire Road, in
the west end of Aberdeen. The court heard that Bowie had been jailed in
Glasgow in 1983 for scalping another woman in Bishopbriggs the previous year."
 Dr Towie allegedly
interrupted the consultation to attend another patient, leaving the
man with his "pants down and sexually aroused" before giving him some
cream and telling him to masturbate."
|
|
Read article...
|
|
Stoning's big comeback
UPI | Submitted by: Ellis D Trippman
"In radical Muslim countries, stoning has experienced a major comeback in
recent years. "Since the inception of the mullahs' rule, hundreds of women
of various ages have been and continued to be stoned to death throughout
Iran," the National Council of Resistance of Iran claimed. One recent such
execution was described in vivid detail by local newspapers: Maryam Ayoubi,
a 38-year-old mother of three, was convicted of adultery and being her
lover's accomplice in her husband's death. The execution occurred on July 11,
2001. According to Iranian press reports, she was first flogged 50 times,
then given a ritual bath, wrapped in a white shroud and carried to the
execution site on a stretcher. There she was buried up to her armpits and
subsequently bombarded with rocks."
|
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Read article...
|
|
Hopped up on methane at Delphi
Washington Post | Submitted by: Nox
"Evidence is growing that the priestesses [of the Oracle at Delphi],
known as pythia, were ripped on hydrocarbon gases, especially ethylene, a
sometime anesthetic which, taken in modest doses, can induce lively
conversation of a somewhat incoherent nature... Low doses induce
isembodied euphoria, with periods of excitation and amnesia, but at
higher doses, you get delirium, hysteria and a combative, agitated state."
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Read article...
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All set, just waiting for the guys to show. My next tweet will be sore for sure.
9/21/2008, 12:50:22 PM
Favs: 0
Retweets: 0
link--- layout: tweet ---
All set, just waiting for the guys to show. My next tweet will be sore for sure.
9/21/2008, 12:50:22 PM
Favs: 0
Retweets: 0
link


Dependency injection
VRaptor is strongly based on Dependency Injection, since all its internal components are
managed using this technique.Tha basic concept behind Dependency Injection (DI) says you should not look for
what you want to access. Instead, it should be provided for you somehow.In Java, this is accomplished by passing components to your controller's constructor.
Suppose your clients controller needs to access a clients Dao. Specify that need in your code:
@Resource
public class ClientController {
private final ClientDao dao;
public ClientController(ClientDao dao) {
this.dao = dao;
}
@Post
public void add(Client client) {
this.dao.add(client);
}
}
And annotate the ClientDao component as a VRaptor @Component
:
@Component
public class ClientDao {
}
From now on, VRaptor will provide your ClientController
with an instance of ClientDao
when needed. Remember that VRaptor will honor the scope specified by the component.
For example, if ClientDao
had specified Session scope (@SessionScoped
),
only one instance of that component would be created per session. (Note that it is
probably wrong to specify session scope for a Dao, it is only a simple example).
ComponentFactory
Sometimes we want our components to receive other libraries' components.
In that case we are unable to change the libraries's source code in order to
annotate its components with @Component
(and any other changes we may need to do).
The most common example is acquiring a Hibernate Session. We need to create a component
that is responsible for providing Session instances for other components that depend on it.
VRaptor has an interface called ComponentFactory
which allows your classes to
provide components.
Classes implementing that interface define a single method. See the following example,
which starts Hibernate when the component is built and uses that configuration to provide
Session instances for our application:
@Component
@ApplicationScoped
public class SessionFactoryCreator implements ComponentFactory<SessionFactory> {
private SessionFactory factory;
@PostConstruct
public void create() {
factory = new AnnotationConfiguration().configure().buildSessionFactory();
}
public SessionFactory getInstance() {
return factory;
}
@PreDestroy
public void destroy() {
factory.close();
}
}
@Component
@RequestScoped
public class SessionCreator implements ComponentFactory<Session> {
private final SessionFactory factory;
private Session session;
public SessionCreator(SessionFactory factory) {
this.factory = factory;
}
@PostConstruct
public void create() {
this.session = factory.openSession();
}
public Session getInstance() {
return session;
}
@PreDestroy
public void destroy() {
this.session.close();
}
}
These implementations are already on VRaptor3. Utils chapter will show you how to use them.
Providers
Behind the curtains, VRaptor uses a specific DI provider and has out-of-the-box support for PicoContainer or Spring DI.
Each implementation give you all you can find in VRaptor's documentation, but also different
extension points.
For letting the providers be able to find your annotated classes, you must set this parameter
on your web.xml, with your app's base package:
<context-param>
<param-name>br.com.caelum.vraptor.packages</param-name>
<param-value>com.yourcompany.yourapp</param-value>
</context-param>
You can set more than one base package, with commas and no spaces:
<context-param>
<param-name>br.com.caelum.vraptor.packages</param-name>
<param-value>com.yourcompany.yourapp,com.another.package</param-value>
</context-param>
It is even possible to have annotated classes inside jars, if your jars include directory
entries ("Add directory entries" on eclipse, or on ant without the option "files only").
Spring
When using Spring, you gain all its features and built-in components to use with VRaptor.
In other words, all components that work with Sprint DI/IoC, also work with VRaptor.
In that case, all the annotations.
You don't have to configure anything, since Spring is the default container.
VRaptor will use your Spring configurations, if you have it already configured in your project
(Context listeners and applicationContext.xml). If VRaptor can't find your Spring configuration
you can configure it, as you can see on Advanced Configurations Chapter.
Pico Container
When using PicoContainer with VRaptor, you will be able to access Pico directly in order
to do advanced configurations.
If you want PicoContainer to be your application's DI provider, put the following entries
in your web.xml descriptor:
<context-param>
<param-name>br.com.caelum.vraptor.provider</param-name>
<param-value>br.com.caelum.vraptor.ioc.pico.PicoProvider</param-value>
</context-param>
Your custom provider
You can also create your own Provider, either to extend the default implementations with PicoContainer or Spring, or to base your implementation in another DI container you may prefer.


Introduction
Welcome to the Pesticide Engagement Portal Companion App. With this application, you can engage with the Pest Management Regulatory Agency. Would you like to make a profile now? This is entirely optional, saved only on your device, and you can change the data at any point.
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We Operate in the countryside of Rukiga, South Western Uganda, where the illiteracy rate is 80% above the national average of 72.2%, teenage pregnancies at 34% and school dropouts are a bad daily dose with 20% of primary school age going children out of school which is above the national average of 12.5% , women and young people are excluded from decision making processes, many young people are still unemployed and living in a country where subsistence agriculture stands at 69.9% and human rights are treated as a luxury.
We target to halve the illiteracy rate in Rukiga from 80% to 65% by the end of 2025 and poverty from 23% that is above the national average of 19% to 18% by the year 2025, reduce youth and women unemployment rate from 83% to 50% by July 2025, reduce the teenage pregnancy rates from 34% that is over the national average of 25% to 23% by October 2025, reduce the number of primary school age going children who do not go to school from 20% that is above the national average of 12.5% to at least 12.5% by the end of 2025, increase the level of participation by young people and women in decision making and/or integration of their issues to at least 60% combined on Rukiga local Government committees by the end of 2025, increase the ratio of commercial farming to subsistence farming from 3:7 to 6:4 by the end of 2025 while ensuring food security.


Mortal Thoughts (1991)
Grade: 80
"Mortal Thoughts" is an excellent crime drama. It is also an unhappy and tense film.
"Mortal Thoughts" stars Demi Moore and Glenne Headly as two lifetime friends who together end up murdering Bruce Willis, Headly's reel-life and Moore's real-life husband. They try to cover up their crime, but are all too aware that they are unlikely to get away with it.
One can argue that Willis has it coming. He is one of filmdom's worst husbands. He is drug-using, abusive, sponging, and obnoxious. He also makes unwanted passes at Moore without taking no for an answer. Clearly, Willis relishes the role.
The story is told mostly in flashbacks. At the start of the film, Moore is under police interrogation by Harvey Keitel. As she gets into increasing trouble attempting to explain her actions, flashbacks take her back to the scenes she is describing. This technique has been used in other crime dramas, such as "Double Indemnity (1944)." There is also a plot twist or two, which is almost mandatory for the genre.
Director Alan Rudolph does fine work. He does has a fascination with slow-motion, particularly when the camera is focusing on Moore.
"Mortal Thoughts" sags a bit in the middle. This may be because we lose all sympathy for Moore and Headly's characters, who are both guilty of various serious crimes.
"Mortal Thoughts" has violent scenes. Other scenes have large amounts of blood everywhere, so it is not for children or the squeamish. One question that has to be asked: Is the violence and blood necessary? I think it is, in this case, because it gets across the point that it is a terrible crime to murder someone. Murder is not glorified in this film.
"Mortal Thoughts" has not received the attention or aclaim that it deserves. I hope that this is not due to the presence of Moore and Willis, who may have made other poor career decisions, but are well-cast here.
http://members.tripod.com/~Brian_Koller/movies.html
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.
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Created by shellcode on 2/6/17.
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| Apache Struts 2 Documentation > Home > Guides > Core Developers Guide > Interceptors > Execute and Wait Interceptor |
The ExecuteAndWaitInterceptor is great for running long-lived actions in the background while showing the user a nice progress meter. This also prevents the HTTP request from timing out when the action takes more than 5 or 10 minutes.
Using this interceptor is pretty straight forward. Assuming that you are including struts-default.xml, this interceptor is already configured but is not part of any of the default stacks. Because of the nature of this interceptor, it must be the last interceptor in the stack.
This interceptor works on a per-session basis. That means that the same action name (myLongRunningAction, in the above example) cannot be run more than once at a time in a given session. On the initial request or any subsequent requests (before the action has completed), the wait result will be returned. The wait result is responsible for issuing a subsequent request back to the action, giving the effect of a self-updating progress meter.
If no "wait" result is found, Struts will automatically generate a wait result on the fly. This result is written in FreeMarker and cannot run unless FreeMarker is installed. If you don't wish to deploy with FreeMarker, you must provide your own wait result. This is generally a good thing to do anyway, as the default wait page is very plain.
Whenever the wait result is returned, the action that is currently running in the background will be placed on top of the stack. This allows you to display progress data, such as a count, in the wait page. By making the wait page automatically reload the request to the action (which will be short-circuited by the interceptor), you can give the appearance of an automatic progress meter.
This interceptor also supports using an initial wait delay. An initial delay is a time in milliseconds we let the
server wait before the wait page is shown to the user. During the wait this interceptor will wake every 100 millis
to check if the background process is done premature, thus if the job for some reason doesn't take to long the wait
page is not shown to the user.
This is useful for e.g. search actions that have a wide span of execution time. Using a delay time of 2000
millis we ensure the user is presented fast search results immediately and for the slow results a wait page is used.
Important: Because the action will be running in a seperate thread, you can't use ActionContext because it is a ThreadLocal. This means if you need to access, for example, session data, you need to implement SessionAware rather than calling ActionContext.getSesion().
The thread kicked off by this interceptor will be named in the form actionNameBrackgroundProcess. For example, the search action would run as a thread named searchBackgroundProcess.
Thread.NORM_PRIORITY
.wait
as result code). Default is no initial delay.If you wish to make special preparations before and/or after the invocation of the background thread, you can extend the BackgroundProcess class and implement the beforeInvocation() and afterInvocation() methods. This may be useful for obtaining and releasing resources that the background process will need to execute successfully. To use your background process extension, extend ExecuteAndWaitInterceptor and implement the getNewBackgroundProcess() method.
<action name="someAction" class="com.examples.SomeAction"> <interceptor-ref name="completeStack"/> <interceptor-ref name="execAndWait"/> <result name="wait">longRunningAction-wait.jsp</result> <result name="success">longRunningAction-success.jsp</result> </action> <%@ taglib prefix="s" uri="/struts" %>   <title>Please wait</title> <meta http-equiv="refresh" content="5;url="/>   Please wait while we process your request. Click <a href=""> if this page does not reload automatically.

 Example code2: This example will wait 2 second (2000 millis) before the wait page is shown to the user. Therefore if the long process didn't last long anyway the user isn't shown a wait page.
 <action name="someAction" class="com.examples.SomeAction"> <interceptor-ref name="completeStack"/> <interceptor-ref name="execAndWait"> <param name="delay">2000<param> <interceptor-ref> <result name="wait">longRunningAction-wait.jsp</result> <result name="success">longRunningAction-success.jsp</result> </action>

 Example code3: This example will wait 1 second (1000 millis) before the wait page is shown to the user. And at every 50 millis this interceptor will check if the background process is done, if so it will return before the 1 second has elapsed, and the user isn't shown a wait page.
 <action name="someAction" class="com.examples.SomeAction"> <interceptor-ref name="completeStack"/> <interceptor-ref name="execAndWait"> <param name="delay">1000<param> <param name="delaySleepInterval">50<param> <interceptor-ref> <result name="wait">longRunningAction-wait.jsp</result> <result name="success">longRunningAction-success.jsp</result> </action>


| Overview | Module | Use | Devguide | Index |
| Services' Summary | Interfaces' Summary | Properties' Summary | Services' Details | Interfaces' Details | Properties' Details |
| service GridControl |
|
| Included Services - Summary | |
| ::com::sun::star::form::FormControlModel |
|
| ::com::sun::star::form::FormComponents | provides basic functionality of a container of ::com::sun::star::form::FormComponents. (details) |
| Exported Interfaces - Summary | |
| ::com::sun::star::form::XGridColumnFactory | allows to create column models to be inserted into the control model. (details) |
| ::com::sun::star::view::XSelectionSupplier | gives access to the currently selection. (details) |
| ::com::sun::star::form::XReset | resets the control. (details) |
| Properties' Summary | |
| short Border |
returns the border style of the control. |
| long BorderColor |
[ OPTIONAL ] specifies the color of the border, if present |
| boolean Enabled |
determines whether the control is enabled or disabled. |
| ::com::sun::star::awt::FontDescriptor FontDescriptor |
contains the font attributes of the text in the control. |
| long RowHeight |
specifies the height of a row of the grid. |
| boolean Tabstop |
determines whether the control can be reached by the tabulator key. |
| ::com::sun::star::util::Color TextColor |
specifies the text color (RGB) of the control. |
| Included Services - Details | |
::com::sun::star::form::FormControlModel
|
|
::com::sun::star::form::FormComponents
|
| Exported Interfaces - Details | |
::com::sun::star::form::XGridColumnFactory
|
|
::com::sun::star::view::XSelectionSupplier
|
|
::com::sun::star::form::XReset
|
| Properties' Details | |
Border
|
|
BorderColor
|
|
Enabled
|
|
FontDescriptor
|
|
RowHeight
|
|
Tabstop
|
|
TextColor
|
Copyright © 2013, The Apache Software Foundation, Licensed under the Apache License, Version 2.0. Apache, the Apache feather logo, Apache OpenOffice and OpenOffice.org are trademarks of The Apache Software Foundation. Other names may be trademarks of their respective owners.


April 2009
The Android 1.5 platform introduces many new features for users and developers. The list below provides an overview of the changes.
User Interface Refinements
- System-wide:
- Refinement of all core UI elements
- Animated window transitions (off by default)
- Accelerometer-based application rotations
- UI polish for:
- In-call experience
- Contacts, Call log, and Favorites
- SMS & MMS
- Browser
- Gmail
- Calendar
- Camera & Gallery
- Application management
Performance Improvements
- Faster Camera start-up and image capture
- Much faster acquisition of GPS location (powered by SUPL AGPS)
- Smoother page scrolling in Browser
- Speedier GMail conversation list scrolling
New Features
- On-screen soft keyboard
- Works in both portrait and landscape orientation
- Support for user installation of 3rd party keyboards
- User dictionary for custom words
- Home screen
- Widgets
- Bundled home screen widgets include: analog clock, calendar, music player, picture frame, and search
- Live folders
- Widgets
- Camera & Gallery
- Video recording
- Video playback (MPEG-4 & 3GP formats)
- Bluetooth
- Stereo Bluetooth support (A2DP and AVCRP profiles)
- Auto-pairing
- Improved handsfree experience
- Browser
- Updated with latest Webkit browser & Squirrelfish Javascript engines
- Copy 'n paste in browser
- Search within a page
- User-selectable text-encoding
- UI changes include:
- Unified Go and Search box
- Tabbed bookmarks/history/most-visited screen
- Contacts
- Shows user picture for Favorites
- Specific date/time stamp for events in call log
- One-touch access to a contact card from call log event
- System
- New Linux kernel (version 2.6.27)
- SD card filesystem auto-checking and repair
- SIM Application Toolkit 1.0
- Google applications (not available in the Android 1.5 System Image that is
included in the Android SDK)
- View Google Talk friends' status in Contacts, SMS, MMS, GMail, and Email applications
- Batch actions such as archive, delete, and label on Gmail messages
- Upload videos to Youtube
- Upload photos on Picasa
New APIs and Manifest Elements
- UI framework
- Framework for easier background/UI thread interaction
- New
SlidingDrawer
widget - New
HorizontalScrollview
widget
- AppWidget framework
- APIs for creating secure home screen
AppWidgets
. For information about how to use AppWidgets, see the Developer's Guide AppWidgets documentation. Also see Introducing home screen widgets and the AppWidget framework on the Android Developer's Blog. - APIs for populating
Live Folders
with custom content.
- APIs for creating secure home screen
- Media framework
- Raw audio recording and playback APIs
- Interactive MIDI playback engine
Video Recording APIs
for developers (3GP format).- Video and photo sharing Intents
- Media search Intent
- Input Method framework
Input Method Service
framework- Text-prediction engine
- Ability to provide downloadable IMEs to users
- Application-defined hardware requirements
Applications can now use a new element in their manifest files, <uses- configuration> to indicate to the Android system what hardware features they require in order to function properly. For example, an application might use the element to specify that it requires a physical keyboard or a particular navigation device, such as a trackball. Prior to installing the application, the Android system checks the attributes defined for the
<uses-configuration>
element and allows the installation to continue only if the required hardware is present. - Speech recognition framework
- Support for using speech recognition libraries via Intent. See
RecognizerIntent
.
- Support for using speech recognition libraries via Intent. See
- Miscellaneous API additions
- LocationManager - Applications can get location change updates via Intent
- WebView - Touch start/end/move/cancel DOM event support
- Redesigned
Sensor Manager APIs
- GLSurfaceView - convenience framework for creating OpenGL applications
- Broadcast Intent for app update install succeeded - for smoother app upgrade experience


To follow up on the small Twitter rant that got people to explain GPG and OTR to me for a whole day, I'll explain the ideas behind this.
There is always a new project around self hosting email and PGP, or building a new encrypted chat app. Let's say it aloud right now: those projects are not trying to improve the situation, they just want to build their own. Otherwise, they would help one of the 150 already existing projects. Worse, a lot of those try to build a business out of it, and end up making a system where you need to completely trust their business (as a side note, building a business is fine, but just be honest about your goals).
There are things that attract new projects, because the concept is easy to get, and this drives developers right into the "I'm smarter than everybody and can do better than existing projects" territory. But there's a reason they fail.
Making an encrypted chat app is solving the same problem as making a chat app -moving people off their existing platform, onto a new one- and additionally writing a safe protocol. Building a whole new infrastructure is not an easy task.
Making email and PGP easier to use is not only a UX issue. There is a complete mental model of how it works, what are the failure modes, and what are the trust levels. And you add above that the issues of email's metadata leaks, the lack of forward secrecy, the key management. Even with the simplest interface, it requires that the user builds that mental model, and that she understands other users may have different models. This is pervasive to the way PGP works. It has always been and will always be an expert's tool, and as such unfit to be deployed massively. You cannot solve infrastructure problems by teaching people.
You cannot solve infrastructure problems by teaching people
There is a pattern here: people want to build tools that will be the basis of safe communications for the years to come. Those are infrastructure problems, like electricity, running water or Internet access. They cannot be solved by teaching people how to use a tool. The tool has to work. They cannot be solved either by decentralization. At some point, someone has to pay for the infrastructure's maintenance. As much as I like the idea of decentralizing everything, this is not how people solve serious engineering problems. But the difference with other types of business is that infrastructure businesses are dumb pipes. They don't care what's running through them, and you can easily replace one with the other.
There is a strong need for "private by default", authenticated, anonymous communication. This will only come if the basic building blocks are here:
- multiparty Off-The-Record(or other protocols like Axolotl): forward secret communication currently only works in two-party communication. Adding more members and making the protocols safe against partitions is a real challenge
- multidevice PGP (or alternate message encryption and authentication system): currently, to use PGP on multiple devices, either you synchronize all your private keys on all your devices, or you accept that some devices will not be able to decrypt messages. This will probably not work unless you have a live server somewhere, or at least some hardware device containing keys
- redundant key storage: systems where a single key holds everything are very seducing, but they're a nightmare for common operation. You will lose the key. And the backup. And the other backup. You will end up copying your master key everywhere, encrypted with a password used infrequently. Either it will be too simple and easily crackable, or too complex and you will forget it. How can a friend or family access the key in case of emergency?
- private information retrieval: PIR systems are databases that you can query for data, and the database will not know (in some margins) which piece of data you wanted. You do not need to go wild on homomorphic encryption to build something useful
- encrypted search: the tradeoffs in search on encrypted data are well known, but there are not enough implementations
- usable cryptography primitives: there is some work in that space, but people still rely on OpenSSL as their goto crypto library. Crypto primitives should not even let you make mistakes
Those are worthwhile targets if you know a fair bit of cryptography and security, and know how to build a complete library or product.
But for products, we need better models for infrastructure needs. A like Tahoe-LAFS is a good model: the user is in control of the data, the provider is oblivious to the content, the user can be the client of multiple providers, and switching from one to another is easy. This does not make for shiny businesses. Infrastructure companies compete mainly on cost, scale, availability, speed, support, since they all provide roughly the same features.
Of course, infrastructure requires standards and interoperability, and this usually does not make good material for startup hype. But everyone wins from this in the end.


“You must be the change you wish to see in the world.”
“Success is not final; failure is not fatal: it is the courage to continue that counts.”
“ In the world of Internet Customer Service, it's important to remember your competitor is only one mouse click away.”
“Sales are contingent on the attitude of the salesman, not the attitude of the prospect”
“Money is a terrible master but an excellent servant.”


Scoreboard
Score Progression
The chart would go here
Rank
Team
School
1
Algorithmic Armadillos -- Since We're the Best
West Windsor-Plainsboro High School South
{{ data|safe }}


Demand for more protective hazmat suits is boosting production at an American chemical and plastics company as fears grow that the Ebola virus may spread. Hospitals and health clinics are being outfitted with coveralls, masks, latex gloves and other equipment because two nurses in Texas became infected with the deadly disease.
And a Pittsburgh-area company wants to sell the public $35 kits with protective gear that could guard it from exposure to the virus.
DuPont, a Wilmington, Del.-based producer of suits and equipment that protect workers from hazardous materials, said it has more than tripled production of its Tyvek and Tychem suits and related apparel since Ebola emerged in West Africa.
The outbreak has sickened more than 9,000 and killed more than 4,500 worldwide.
“We will continue our best efforts to contribute to the global response,” company spokeswoman Sandra James said.
The company ramped up production in recent months when orders from the World Health Organization and groups dealing with the outbreak in West Africa increased, James said.
She declined to say how many of the suits DuPont is making or how much they cost. An online search of Tyvek and Tychem suits show prices between $10 and $15 each.
Lakeland Industries and 3M Co. manufacture protective suits and equipment infectious disease workers use. Neither company would comment on Ebola-related demand.
Nurses have complained that hospitals are not prepared to treat cases of Ebola in the United States, lacking training, equipment and plans.
National Nurses United, with 185,000 members, said 37 percent of nurses said their hospitals had insufficient supplies of eye protection; 36 percent said they didn't have enough impermeable suits, according to the group's survey of more than 2,000 nurses in 46 states.
The union is calling for a national standard for personal protection equipment that would protect health care workers from exposure.
The Centers for Disease Control and Prevention recommended stricter guidelines for protective suits that ensure health care workers fully protect their hair and skin.
Pittsburgh-area hospitals say they have enough of the most-protective equipment to deal with Ebola, which is transmitted through direct contact with bodily fluids of an infected person.
UPMC and Allegheny Health Network hospitals stock a wider variety of protective gear, including hooded full-body suits that would shield workers from Ebola, doctors at the health systems said. The gear should provide more protection than more piecemeal combinations of gowns and gloves, officials said.
“Barriers work, but you have to use them effectively,” said Dr. Carlene Muto, infection control director at UPMC, which is ramping up worker training for the equipment.
The system of more than 20 hospitals declined to say how many of the suits it bought. Dan Laurent, a spokesman for Allegheny Health, said the seven-hospital network ordered suits, but declined to say how many.
Lora Regan, medical director of corporate health for Lancaster General Health and president of the Pennsylvania Occupational and Environmental Medicine Society, said the state's hospitals and other health facilities are buying more of the most-protective gear, including impermeable one-piece suits with integrated booties and hoods.
UPMC and Allegheny Health Network hospitals stock a wider variety of protective gear, including hooded full-body suits that would shield workers from Ebola, doctors at the health systems said. The gear should provide more protection than more piecemeal combinations of gowns and gloves, officials said.
DuPont makes the most popular versions that follow CDC guidelines, she said.
The focus needs to be on training and rehearsal of the proper way for health care workers to put on and take off the suits to avoid contamination, Regan said. The CDC is investigating how two nurses at Texas Health Presbyterian became infected with Ebola even though they wore protective gear.
“There's a lot of interest in the de-gowning process and making sure that it's done correctly,” she said.
Weavertown Environmental Group, a Carnegie-based hazardous material handling company, is putting together kits of protective equipment that it hopes to sell to the public, perhaps later this week.
The company expects the kit to include a suit, mask and gloves for about $35, though the materials haven't been ordered.
Weavertown is preparing to help hospitals and homeowners that may need cleaning of infected areas.
Dealing with Ebola or other infectious diseases isn't different from cleaning up other dangerous material, such as spills of toxic chemicals or decontaminating a home that was used as a meth lab or where a murder or suicide occurred.
Alex Nixon is a staff writer for Trib Total Media. He can be reached at<PHONE_NUMBER> or anixon@tribweb.com.


--- layout: post status: publish published: true title: Kris Bryant Promoted to Iowa Cubs author: display_name: Corey Fineran login: Corey email<EMAIL_ADDRESS>url: '' author_login: Corey author_email<EMAIL_ADDRESS>wordpress_id: 10134 wordpress_url: http://ivyenvy.com/?p=10134 date: '2014-06-18 11:35:03 +0000' date_gmt: '2014-06-18 16:35:03 +0000' categories: - Prospects tags: - Iowa Cubs - Christian Villanueva - Kris Bryant - Tennesse Smokies comments: - id: 11624 author: Dawn Strand author_email: '' author_url: '' date: '2014-06-18 11:41:19 +0000' date_gmt: '2014-06-18 16:41:19 +0000' content: Wahoo! - id: 11630 author: Kurtis Tucker author_email<EMAIL_ADDRESS>author_url: '' date: '2014-06-19 08:53:36 +0000' date_gmt: '2014-06-19 13:53:36 +0000' content: The extra year of team control will be even more important with Bryant. No Chicago until next year. He's under Scott Boras and Scott Boras does NOT do lock them in early for a discount deals. - id: 11631 author: Kurtis Tucker author_email: '' author_url: '' date: '2014-06-19 08:54:33 +0000' date_gmt: '2014-06-19 13:54:33 +0000' content: Kap and Haugh interviewed him yesterday. http://thegamechicago.com/2014/06/18/kris-bryant-learning-of-aaa-promotion-part-of-a-whirlwind-day/#axzz355mdMTtC ---
— Carrie Muskat (@CarrieMuskat) June 18, 2014
Recently on the podcast, we discussed that Bryant had only been in Double-A Tennessee for two months and in the words of Ian Stewart, the Cubs were not letting Kris Bryant "rott" in Double-A. I think the Cubs wanted to give Bryant the All-Star experience.
If the Cubs were waiting for Kris Bryant to struggle in Double-A, I don't think it was going to happen. Bryant hit .355/.458/.702 with 22 home runs and was crowned the Southern League's Home Run Derby winner.
One thing that makes this promotion interesting is where players are plugged in defensively. Christian Villanueva is the Cubs #11 prospect (according to MLB.com) and has the best defensive skills of the potential candidates to occupy 3rd base for the Cubs in the future.
It's possible that Bryant could end up in a corner outfield spot. I'm guessing the Cubs would rather take at-bats from Josh Vitters in LF rather than from Villanueva at 3rd base. What is a given is that Bryant will get his at-bats. The Cubs did not call him up to platoon with another player.
In many ways a move in the next week or so was completely predictable. On the other hand, I think a promotion right now has the potential to lead to a sticky situation later this season. I don't think the Cubs had any intention of calling up Kris Bryant to the big league team this season. I think they'd much prefer to wait until after the Super-Two Status cut-off next season. If you don't understand what the Super-Two Status is or why the Cubs would benefit from waiting to call up Kris Bryant, or any other prospect after this cut-off, read the piece I wrote on Javier Baez and Super-Two Status.
The Bryant promotion is exciting for Cubs fans, though. The top two prospects in the system are at the top level of the minor leagues. My hope is that Bryant is forced to develop a lot at Triple-A, similar to the development that Javier Baez is going through. It's best for the organization, and us fans, if the players can be challenged at some point in the minors. I think it'd be safe to say that Kris Bryant has probably never struggled at any level. I'd rather him learn how to work through things and make adjustments in AAA rather than experiencing those things for the first time with MLB pitching.
The Iowa Cubs should have no problems bringing in fans with Javier Baez and Kris Bryant both on the roster. If things go as planned, 2015 could be a really interesting season in this rebuilding process with potentially some big steps forward.


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Risk and Vulnerability can be assessed in terms of the characteristics and interactions between Drivers or Pressures, and Stocks or Capitals. Drivers, or Pressures, result from change in the natural or human environment, and can manifest in the short term (or very short term, as is the case with disasters), medium term, or long term. Pressures result from the change in intensity, frequency or quantity of a driver away from its typical baseline. Stocks, or Capitals, are the things that we value: human lives and livelihoods, infrastructure, economic value, and ecosystem services, to name a few.
Observation underpins the entire conceptual framework, allowing us to perceive changes in Driver functions and resulting Hazards and their frequency as well as corresponding responses or Impacts on Stocks.
 Specifically, the same Pressure (say rising flood water) can lead to different Hazards if different Stocks are Exposed. When a Stock is Exposed to a Hazard, only the subset of the Stock that is Susceptible will be Impacted. Impact is generally defined as the fraction of the Stock that is Exposed, times the fraction of the Stock that is Susceptible.
The frequency and/ or probability of a Hazard occurring, in addition to the Impact, determines Risk. If frequency of a Hazard is high, but the Impact is low, the Risk is low, and vice versa. Likewise, Vulnerability or Coping Capacity determines if the Risk is an issue or not. If Risk is high but Coping Capacity is a maximum, it is far less of a problem than if Coping Capacity is low or non-existent.
Finally, Mitigation and Adaptation measures can be focused on modification of multiple elements of the framework to preserve or improve the state of Stocks.


Because multiple intercepters were set, you've been sent here where you can open new windows for each intercepter.


August 11
And [God] brought [Abram] forth abroad, and said, look now toward heaven, and tell the stars, if thou be able to number them: and he said unto him, so shall thy seed be. And he believed in the Lord; and he counted it to him for righteousness.
Genesis 15:5-6
Do you ever have trouble believing the Word of God? Not just agreeing with it mentally, but really believing that what it says will work for you? I do. There are times when the promises in the Word stagger my mind. There have been times when I've felt so defeated and the circumstances around me looked so bad that it was tough for me to believe I was "more than a conqueror" even though I knew God said I was. What do you do when your mind staggers like that at the promise of God? You meditate on that promise.
Scriptural meditation simply means thinking about and reflecting on the Word of God.  That kind of meditation can affect your life in a way that almost nothing else can. It can, quite literally, alter your mind. That's what happened to Abram. When God first told him that he was going to father a nation, he was an old man. His wife, Sara, was also old. What's more, she had been barren all her life. How could an aging, childless couple have even one child--much less a nation full of them? Abram couldn't even imagine such a thing. It contradicted his entire mind-set.
But God knew the mental struggle Abram would have, so He didn't just make him a verbal promise and leave it at that. He gave Abram a picture of that promise to meditate on. He took him out into the starry night, turned his eyes to the sky and said, "So shall thy seed be." Can't you just see Abram staring out at the stars, trying to count them? Filling the eyes of his heart with the promise of God?
That's what meditation is all about. Taking time to envision the promise of God until it becomes a reality inside you. It's tremendously powerful, and by focusing on the scriptural promises God has given you, you can put it to work in your life just as Abram put it to work in his.
Don't just read the Word. Meditate on it today.
Scripture Reading: Romans 4:13-25


THIS SCATTERPLOT SHOWS THE 30 TOP ARTISTS FOR THE SPECIFIED GENRE ACCORDING TO THE ECHO NEST DATABASE. THE POSITION OF AN ARTIST IN THIS SCATTERPLOT IS BASED ON BOTH ARTIST POPULARITY AND FAMILIARITY.  HOVERING OVER AN ARTIST SHOWS THE ARTISTS NAME AND SELECTING AN ARTIST PROVIDES RELATED INFORMATION (LINK TO THE LAST FM ARTIST PAGE, PICTURE, AND SMART RADIO MUSIC PLAYER.
WHEN SELECTING AN ARTIST, A NEW REQUEST TO THE ECHO NEST DATABASE IS MADE WHICH RETURNS 30 RELATED ARTISTS FOR THE SELECTED ARTIST. THESE ARTISTS ARE ALSO SORTED OUT BY POPULARITY AND FAMILIARITY.


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The following tutorials will help you to get familiar with RoBO.
You can find the code for all the tutorials and additional examples
in the examples
folder.
This tutorial will show you how to use standard Bayesian optimization with Gaussian processes and different acquisition functions to find the global minimizer of your (python) function. Note that RoBO so far only support continuous input space and it is not able to handle multi-objective functions.
The first thing we have to do is to import numpy (for the objective function) and the bayesian_optimization interface
import numpy as np from robo.fmin import bayesian_optimization
To use RoBO we have to define a function that symbolizes the objective function we want to minimize. Interface of objective function is fairly simple, it gets an d-dimensional vector x and returns the corresponding scalar function value.
def objective_function(x): y = np.sin(3 * x[0]) * 4 * (x[0] - 1) * (x[0] + 2) return y
Before we run Bayesian optimization we first have to define the lower and upper bound of our input search space. In this case our search space contains only one dimension, but Bayesian optimization is not restricted to that and normally works fine up to 10 (continuous) dimensions.
lower = np.array([0]) upper = np.array([6])
Now we have everything we need and can now run Bayesian optimization for 50 iterations:
results = bayesian_optimization(objective_function, lower, upper, num_iterations=50)
At the end we get a dictionary back with contains the following entries:
By default RoBO uses Gaussian processes (with MCMC sampling to obtain the GP’s hyperparameters) and logarithmic expected improvement as acquisition function. If you would like to use a different acquisition function such as for instance the lower confidence bound you can simple :
results = bayesian_optimization(objective_function, lower, upper, acquisition_func='lcb')
See the API documentation for different possible choices of acquisition functions.
If you want to have a deeper look what RoBO is doing under the hood you can activate RoBO’s logging mechanism by adding the following two lines on top of your python script:
import logging logging.basicConfig(level=logging.INFO)
Besides standard Bayesian optimization, RoBO also contains an interface for plain random search and entropy search by Hennig et. al. Both methods follow the exact same interface.
from robo.fmin import entropy_search from robo.fmin import random_search results = entropy_search(objective_function, lower, upper) results = random_search(objective_function, lower, upper)
RoBO offers an simple interface for Bayesian Optimization with Hamiltonian Monte Carlo Artificial Neural Networks (BOHAMIANN) which was introduced by Sprigenberg et al.
If you want to use Bohamiann make sure that you have Lasagne and Theano installed:
pip install Lasagne pip install theano
and that the sgmcmc package is in your PYTHONPATH:
The interface to Bohamiann is exactly the same as for the GP based Bayesian optimization interface:
from robo.fmin import bohamiann results = bohamiann(objective_function, lower, upper, num_iterations=50)
This will return a dictionary with the same meta information as described above.
@inproceedings{springenberg-nips2016, booktitle = {Advances in Neural Information Processing Systems 29}, month = {December}, title = {Bayesian optimization with robust Bayesian neural networks}, author = {J. T. Springenberg and A. Klein and S.Falkner and F. Hutter}, year = {2016} }
The idea of Fabolas (Klein et al.) is to take the training data set size as an additional input into account that can be freely chosen during the optimization procedure but is fixed afterwards. The idea is to speed up the optimization by evaluating single configurations only on much cheaper subset and to extrapolate their performance on the full dataset.
By additionally modelling the cost of training single configurations, Fabolas uses the information gain per unit cost to pick and evaluate configurations on small subset of the training data that give the most information about the global minimum on the full dataset.
Now the objective function gets besides a configuration also the training dataset size as an additional input. After training the configuration on a subset of the training data it returns the validation error on the full validation data set as well as the time it took to train this configuration.
from robo.fmin import fabolas def objective_function(x, s): # Train your algorithm here with x on the dataset subset with length s # Estimate the validation error and the cost on the validation data set return validation_error, cost
Additionally you have to define the bounds of the input space for the configurations and the minimum and maximum data set size.
lower = np.array([-10, -10]) upper = np.array([10, 10]) s_min = 100 s_max = 50000
Then you can call Fabolas by:
res = fabolas(objective_function, lower=lower, upper=upper, s_min=s_min, s_max=s_max, num_iterations=100)
You can find a full example for training a support vector machine on MNIST here
@article{klein-corr16, author = {A. Klein and S. Falkner and S. Bartels and P. Hennig and F. Hutter}, title = {Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets}, journal = corr, llvolume = {abs/1605.07079}, lurl = {http://arxiv.org/abs/1605.07079}, year = {2016} }
HPOlib2 contains a set of benchmarks with an unified interface for hyperparameter optimization of machine learning algorithms. In the following example we want to assume the often used synthetic function branin. Make sure that you installed HPOlib2.
First we load the benchmark and get the bound of the configuration space
from hpolib.benchmarks.synthetic_functions import Branin f = Branin() info = f.get_meta_information() bounds = np.array(info['bounds'])
Than we can simply run RoBO by:
results = bayesian_optimization(f, bounds[:, 0], bounds[:, 1], num_iterations=50)
HPOlib2 allows to evaluate single configurations only subsets of the data which allows us to use Fabolas or MTBO. If want to use Fabolas to optimize let’s say a support vector machine on MNIST we first have to wrap the HPOlib2 benchmarks class in order to pass the correct ration of the dataset size:
from hpolib.benchmarks.ml.svm_benchmark import SvmOnMnist f = SvmOnMnist() def objective(x, s): dataset_fraction = s / s_max res = f.objective_function(x, dataset_fraction=dataset_fraction) return res["function_value"], res["cost"]
Than we can run Fabolas simply by:
info = f.get_meta_information() bounds = np.array(info['bounds']) lower = bounds[:, 0] upper = bounds[:, 1] results = fabolas(objective_function=objective, lower=lower, upper=upper, s_min=100, s_max=s_max, n_init=10, num_iterations=80, n_hypers=20, subsets=[64., 32, 16, 8])
The following tutorial we will see how we can train a Bayesian neural networks with stochastic MCMC sampling on our dataset. Note all models in RoBO implement the same interface and you can easily replace the Bayesian neural network by another model (Gaussian processes, Random Forest, …).
Assume we collect some data point of a sinc function:
import matplotlib.pyplot as plt import numpy as np from robo.models.bnn import BayesianNeuralNetwork from robo.initial_design.init_random_uniform import init_random_uniform def f(x): return np.sinc(x * 10 - 5).sum(axis=1) X = init_random_uniform(np.zeros(1), np.ones(1), 20, rng) y = f(X)
We can now create and train a neural network by:
model = BayesianNeuralNetwork(sampling_method="sghmc", l_rate=np.sqrt(1e-4), mdecay=0.05, burn_in=3000, n_iters=50000, precondition=True, normalize_input=True, normalize_output=True) model.train(X, y)
After training we can use our model to predict the mean and variance for arbitrary test points:
x = np.linspace(0, 1, 100)[:, None] mean_pred, var_pred = model.predict(x)
In the following tutorial, we will show how to use RoBO with FANOVA (Functional Analysis of Variance). FANOVA is a framework used for quantifying the performance of functions. More information of FANOVA can be found here .
For this example, let’s use Brannin as our objective function and random search as our optimization method. First we have to import all the necessary packages that we will need.
from fanova import fANOVA import numpy as np from robo.fmin import random_search from hpolib.benchmarks.synthetic_functions import Branin import fanova.visualizer
Now we declare the objective function and get the bounds of the configuration space.
objective_function = Branin() info = objective_function.get_meta_information() bounds = np.array(info['bounds']) config_space = objective_function.get_configuration_space()
Now we apply random search
results = random_search(objective_function, bounds[:, 0], bounds[:, 1], num_iterations=50)
After getting the results, we create a FANOVA object and pass the results to it.
X = np.array([i for i in results['X']]) Y = np.array([i for i in results['y']]) f = fANOVA(X,Y)
To compute now the marginal of the first parameter, we can wrtie:
print(f.quantify_importance((0, )))
To visualize the single and pairwise marginals, we have to create a visualizer object first containing the fanova object, configspace and directory.
vis = fanova.visualizer.Visualizer(f, config_space, "./plots/")
We can then plot single marginals by
vis.plot_marginal(1)
Which should look like:


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