skl_to_pmml module¶
-
skl_to_pmml.
get_PMML_kwargs
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns all the pmml elements.
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the target column .
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: algo_kwargs – Get the PMML model argument based on scikit learn model object
Return type: Dictionary
-
skl_to_pmml.
get_bayes_inputs
(model, derived_col_names)[source]¶ It returns the Bayes Input element of the model .
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing.
Returns: Returns a BayesInput instance.
Return type: bayes_inputs
-
skl_to_pmml.
get_bayes_output
(model, target_name)[source]¶ It returns the Bayes Output element of the model
Parameters: - model – An instance of Scikit-learn model.
- target_name (String) – Name of the Target column.
Returns: Returns a BayesOutput instance
Return type:
-
skl_to_pmml.
get_categoric_pred
(row_idx, der_fld_idx, model_coef, class_lbls, class_attribute)[source]¶ Parameters: - row_idx (int) – Contains an integer value to index attribute/column names
- der_fld_idx (int) – Contains an integer value to differentiate between linear and svm models
- model_coef (array) – Contains the estimators coefficient values
- class_lbls (list) – Contains the list of categorical values
- class_attribute (tuple) – Contains Categorical attribute name
Returns: categoric_predictor – Returns a list with instances of nyoka categorical predictor class
Return type: list
-
skl_to_pmml.
get_classid
(class_attribute, feat_name)[source]¶ Parameters: - class_attribute – Contains the name of the attribute/column that contains categorical values
- feat_name (string) – Contains the name of the attribute/column
Returns: class_idx – Returns an integer value that will represent each categorical value
Return type: int
-
skl_to_pmml.
get_classificationMethod
(model)[source]¶ It returns the Classification Model name of the model.
Parameters: model – A Scikit-learn model instance. Returns: Return type: Returns the classification method of the SVM model
-
skl_to_pmml.
get_comparison_measure
(model)[source]¶ It return the Comparison measure element.
Parameters: model – An instance of Scikit-learn model. Returns: Returns a ComparisonMeasure instance. Return type: comp_measure
-
skl_to_pmml.
get_data_dictionary
(model, feature_names, target_name, categoric_values)[source]¶ It returns the Data Dictionary element.
Parameters: - model – A Scikit-learn model instance.
- feature_names (List) – Contains the list of feature/column name.
- target_name (List) – Name of the Target column.
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: Return the dataDictionary instance
Return type: data_dict
-
skl_to_pmml.
get_dtype
(feat_value)[source]¶ It return the data type of the value.
Parameters: feat_value – Contains a value for finding the its data type. Returns: Return type: Returns the respective data type of that value.
-
skl_to_pmml.
get_ensemble_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Mining Model element of the model
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value.
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: mining_models – Returns the MiningModel of the respective ensemble model
Return type: List
-
skl_to_pmml.
get_funct
(sk_model)[source]¶ It returns the activation fucntion of the model.
Parameters: model – A Scikit-learn model instance. Returns: a_fn – Returns the activation function. Return type: String
-
skl_to_pmml.
get_header
()[source]¶ It returns the Header element of the pmml.
- header :
- Returns the header of the pmml.
-
skl_to_pmml.
get_inline_table
(model)[source]¶ It Returns the Inline Table element of the model.
Parameters: model – An instance of Scikit-learn model. Returns: Returns a InlineTable instance. Return type: InlineTable
-
skl_to_pmml.
get_inner_segments
(model, derived_col_names, col_names, index)[source]¶ It returns the Inner segments of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- index (Integer) – The index of the estimator for the model
Returns: segments – Get the Segments for the Segmentation element.
Return type: List
-
skl_to_pmml.
get_instance_fields
(derived_col_names, target_name)[source]¶ It returns the Instance field element.
Parameters: - derived_col_names (List) – Contains column names after preprocessing.
- target_name (String) – Name of the Target column.
Returns: Returns a InstanceFields instance
Return type:
-
skl_to_pmml.
get_kernel_type
(model)[source]¶ It returns the kernel type element.
Parameters: model – A Scikit-learn model instance. Returns: kernel_kwargs – Get the respective kernel type of the SVM model. Return type: Dictionary
-
skl_to_pmml.
get_knn_inputs
(col_names)[source]¶ It returns the KNN Inputs element.
Parameters: col_names (List) – Contains list of feature/column names. Returns: Returns a KNNInputs instance. Return type: KNNInputs
-
skl_to_pmml.
get_mining_func
(model)[source]¶ It returns the name of the mining function of the model.
Parameters: model – A Scikit-learn model instance. Returns: func_name – Returns the function name of the model Return type: String
-
skl_to_pmml.
get_mining_schema
(model, feature_names, target_name, mining_imp_val)[source]¶ It returns the Mining Schema of the model.
Parameters: - model – A Scikit-learn model instance.
- feature_names (List) – Contains the list of feature/column name.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value.
Returns: Get the MiningSchema element
Return type:
-
skl_to_pmml.
get_model_kwargs
(model, col_names, target_name, mining_imp_val)[source]¶ It returns all the model element for a specific model.
Parameters: - model – An instance of Scikit-learn model.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
Returns: model_kwargs – Returns functionname, MiningSchema and Output of the sk_model object
Return type: Dictionary
-
skl_to_pmml.
get_multiple_model_method
(model)[source]¶ It returns the name of the Multiple Model Chain element of the model.
Parameters: model – A Scikit-learn model instance Returns: Return type: The multiple model method for a mining model.
-
skl_to_pmml.
get_naiveBayesModel
(model, derived_col_names, col_names, target_name, mining_imp_val)[source]¶ It returns the Naive Bayes Model element of the model.
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
Returns: naive_bayes_model – Returns the NaiveBayesModel
Return type: List
-
skl_to_pmml.
get_nearestNeighbour_model
(model, derived_col_names, col_names, target_name, mining_imp_val)[source]¶ It returns the Nearest Neighbour model element.
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
Returns: Returns a nearest neighbour model instance
Return type: nearest_neighbour_model
-
skl_to_pmml.
get_neural_layer
(model, feature_names, target_name)[source]¶ It returns the Neural Layer and Neural Ouptput element.
Parameters: - model – A Scikit-learn model instance.
- feature_names (List) – Contains the list of feature/column name.
- target_name (String) – Name of the Target column.
Returns: - all_neuron_layer (List) – Return the list of NeuralLayer elelemt.
- neural_output_element – Return the NeuralOutput element instance
-
skl_to_pmml.
get_neural_models
(model, derived_col_names, col_names, target_name, mining_imp_val)[source]¶ It returns Neural Network element of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value.
Returns: neural_model – Model attributes for PMML file.
Return type: List
-
skl_to_pmml.
get_neuron_input
(feature_names)[source]¶ It returns the Neural Input element.
Parameters: feature_names (List) – Contains the list of feature/column name. Returns: Returns the NeuralInputs element Return type: neural_input_element
-
skl_to_pmml.
get_node
(model, features_names, main_model=None)[source]¶ It return the Node element of the model.
Parameters: - model – An instance of the estimator of the tree object.
- features_names (List) – Contains the list of feature/column name.
- main_model – A Scikit-learn model instance.
Returns: Get all the underlying Nodes.
Return type: _getNode
-
skl_to_pmml.
get_numeric_pred
(row_idx, der_fld_idx, model_coef, der_fld_name)[source]¶ Parameters: - row_idx (int) – Contains an integer value to index attribute/column names
- der_fld_idx (int) – Contains an integer value to differentiate between linear and svm models
- model_coef (array) – Contains the estimators coefficient values
- der_fld_name (string) – Contains the name of the attribute
Returns: Returns an instances of nyoka numeric predictor class
Return type: num_pred
-
skl_to_pmml.
get_outer_segmentation
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Segmentation element of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: A segmentation instance.
Return type: segmentation
-
skl_to_pmml.
get_output
(model, target_name)[source]¶ It returns the output element of the model.
Parameters: - model – A Scikit-learn model instance.
- target_name (String) – Name of the Target column.
Returns: Get the Output element.
Return type:
-
skl_to_pmml.
get_regr_predictors
(model_coef, row_idx, feat_names, categoric_values)[source]¶ Parameters: - model_coef (array) – Contains the estimators coefficient values
- row_idx (int) – Contains an integer value to differentiate between linear and svm models
- feat_names (list) – Contains the list of feature/column names
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: predictors – Returns a list with instances of nyoka numeric/categorical predictor class
Return type: list
-
skl_to_pmml.
get_regrs_models
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Regression Model element of the model
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: regrs_models – Returns a regression model of the respective model
Return type: List
-
skl_to_pmml.
get_regrs_tabl
(model, feature_names, target_name, categoric_values)[source]¶ It returns the Regression Table element of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- target_name (String) – Name of the Target column.
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: merge – Returns a list of Regression Table.
Return type: List
-
skl_to_pmml.
get_segments
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns the Segment element of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: A list of segment instances.
Return type: segments
-
skl_to_pmml.
get_segments_for_gbc
(model, derived_col_names, col_names, target_name, mining_imp_val, categoric_values)[source]¶ It returns list of Segments element of the model.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: segments – Get the Segments for the Segmentation element.
Return type: List
-
skl_to_pmml.
get_super_cls_names
(model_inst)[source]¶ It returns the set of Super class of the model.
- model_inst:
- Instance of the scikit-learn model
Returns: parents – Returns all the parent class of the model instance. Return type: Set
-
skl_to_pmml.
get_supportVectorMachine
(model)[source]¶ It return the Support Vector Machine element.
Parameters: model – A Scikit-learn model instance. Returns: support_vector_machines – Get the Support Vector Machine element which conatains targetCategory, alternateTargetCategory, SupportVectors, Coefficients Return type: List
-
skl_to_pmml.
get_supportVectorMachine_models
(model, derived_col_names, col_names, target_names, mining_imp_val, categoric_values)[source]¶ It returns the Support Vector Machine Model element.
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_names (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: supportVector_models – Returns SupportVectorMachineModel elements which contains classificationMethod, VectorDictionary, SupportVectorMachine, kernelType
Return type: List
-
skl_to_pmml.
get_targets
(model, target_name)[source]¶ It returns the Target element of the model.
Parameters: - model – A Scikit-learn model instance.
- target_name (String) – Name of the Target column.
Returns: Returns a Target instance.
Return type: targets
-
skl_to_pmml.
get_threshold
()[source]¶ It returns the Threshold value.
Returns: Return type: Returns the Threshold value
-
skl_to_pmml.
get_training_instances
(model, derived_col_names, target_name)[source]¶ It returns the Training Instance element.
Parameters: - model – An instance of Scikit-learn model.
- derived_col_names (List) – Contains column names after preprocessing
- target_name (String) – Name of the Target column.
Returns: Returns a TrainingInstances instance
Return type:
-
skl_to_pmml.
get_tree_models
(model, derived_col_names, col_names, target_name, mining_imp_val)[source]¶ It return Tree Model element of the model
Parameters: - model – A Scikit-learn model instance.
- derived_col_names – Contains column names after preprocessing.
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the Target column.
- mining_imp_val (tuple) – Contains the mining_attributes,mining_strategy, mining_impute_value
Returns: tree_models – Get the TreeModel element.
Return type: List
-
skl_to_pmml.
get_vectorDictionary
(model, derived_col_names, categoric_values)[source]¶ It return the Vector Dictionary element.
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: A Vector Dictionary instance.
Return type:
-
skl_to_pmml.
get_vectorfields
(model_coef, feat_names, categoric_values)[source]¶ It return the Vector Fields .
Parameters: - model – A Scikit-learn model instance.
- derived_col_names (List) – Contains column names after preprocessing.
- categoric_values (tuple) – Contains Categorical attribute names and its values
Returns: Return type: Returns the Vector Dictionary instance for Support Vector model.
-
skl_to_pmml.
get_version
()[source]¶ It returns the pmml version .
Returns: version – Returns the version of the pmml. Return type: String
-
skl_to_pmml.
is_labelbinarizer
(feat_name)[source]¶ Parameters: feat_name (string) – Contains the name of the attribute Returns: Return type: Returns a boolean value that states whether label binarizer has been applied or not
-
skl_to_pmml.
is_stdscaler
(feat_name)[source]¶ Parameters: feat_name (string) – Contains the name of the attribute Returns: Return type: Returns a boolean value that states whether standard scaler has been applied or not
-
skl_to_pmml.
skl_to_pmml
(pipeline, col_names, target_name, pmml_f_name='from_sklearn.pmml')[source]¶ Exports scikit-learn pipeline object into pmml
Parameters: - pipeline – Contains an instance of Pipeline with preprocessing and final estimator
- col_names (List) – Contains list of feature/column names.
- target_name (String) – Name of the target column.
- pmml_f_name (String) – Name of the pmml file. (Default=’from_sklearn.pmml’)
Returns: Return type: Returns a pmml file


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Coutesy of UCR/California Museum of Photography
http://www.ark.cdlib.org/ark:/13030/kt8p3017r3/


--- layout: post title: ! 'InfoQ: The Story of a Project' published: true date: 2010-09-01 categories: [] posterous_url: http://blog.hungerandthirstdesign.com/articles/mallassi-project-story posterous_slug: articles/mallassi-project-story --- http://www.infoq.com/articles/mallassi-project-story?utm_source=twitterfeed&a...
Interesting take on how project lifecycle has changed


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{% load i18n %}{% blocktrans with id=exemplar.pk title=exemplar.book.title %}
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One of your book (Title: {{title}}, exemplar id: {{id}}) has been sold ! You can come to AGEPoly's desk to claim your money.
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(This email is sent automaticaly when a book is sold){% endblocktrans %}


Welcome to my site
Hello there! This is my personal website. If you are here, you probably want to check out some of these comics:
Ghostcat ComicsHamlet's Danish
Rob and Elliot
Hello there! This is my personal website. If you are here, you probably want to check out some of these comics:
Ghostcat Comics


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interface JsonConstructionCallback
Callback used by the calling user to get the result from the json creation
onJsonCreatedSuccessful
abstract fun onJsonCreatedSuccessful(json: JSONObject): Unit
Callback called when json is successfully created


Knives Out Movie Review: This Daniel Craig-Chris Evans Starrer Makes For A Decent Watch
by Roktim RajpalRating: 3.0/5 Star Cast: Daniel Craig, Chris Evans, Ana de Armas Director: Rian Johnson
Daniel Craig, one of the biggest names in Hollywood, is back with his latest release Knives Out, which arrived in theatres today (November 29) and gave moviegoers a reason to rejoice. The film, directed by Rian Craig Johnson, has created a reasonable amount of buzz amongst fans and this makes it a crucial release for all concerned. So, did the murder-mystery deliver the goods and entertain cinemagoers? Read the Knives Out movie review to find out.
Plot
The film revolves around the unexpected events that unfold when a self-made tycoon is found dead under 'suspicious circumstances'. Things turn murkier when a debonair investigator/detective enters the scene and learns one big secret after the other.
Screenplay
Knives Out starts off on a shocking note, which might leave movie buffs stunned. Thereafter, it plays out like a conventional murder mystery and manages to keep die-hard fans of the genre hooked. The interrogation scenes have been executed well and prove to be the backbone of the first half. They also add depth to the characters, which enhances the experience. The second half, however, is not as good as expected. While the initial portions are good, things soon become predictable and this dilutes the impact of the 'big twist' towards the end.
Performances
Daniel Craig is the heart and soul of Knives Out and elevates the onscreen action with his suave dialogue delivery and charming screen presence. Chris Evans is okay but gets overshadowed by 'James Bond'. Christopher Plummer is simply fabulous and conveys the loneliness of his character quite well.
Ana de Armas impresses big time, 'throwing up' a splendid act. Some of the sequences involving her character have situational humour and this works in her favour. The rest of the cast members are pretty good and carry their roles rather well.
Technical Departments
The movie's background score is not too effective, failing to elevate the key scenes. However, the editing is reasonably good as none of the sequences drag. Cinematography is good, especially in the opening sequence. The other technical departments have been handled well.
Pros
Daniel Craig's 'Sherlock' Act
Solid Opening Sequence
Sequences Involving Ana de Armas and Christopher Plummer
Competent Editing
Cons
Predictable Second Half
Weak Background Score
Chris Evans Is Under-utilised
The Verdict
Knives Out is a predictable but sincere movie that should click with the target audience.


Advanced Services Engineering
Overview
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1
Course Overview
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2
Emerging dynamic distributed systems and challenges for advanced services engineering
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3
Data-as-a-Service - Concepts, Design and Implementation, and Ecosystems
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4
Data concerns
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5
Evaluating data concerns
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6
Data marketplace models and concepts
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7
Advanced service-based data analytics: concepts and designs
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8
Quality of data -aware data analytics workflows
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9
Engineering human-based services in data analytics
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---
layout: page
title: (CG) Chess Set 3D (THREE.js)
---
3D Chess Set
Goal: Create a 3D chess set using 3D modelling
Time: 2 weeks
Requirements:
Model 3D figures using LatheGeometry
Create knight figure using ExcludeGeometry
Cut the bishop's top using binary operations
Save figures' positions using JSON and set them accordingly in the scene
Implement spotlight/directional light with interactive parameters and shadows
Solution:
Models were created using this JSON file
Textures were applied to tabletop, chess board and all pieces except for the knights
Bishop was cut by creating a rotated cube and subtracting it from bishop's mesh
Implemented an interactive shadow and spotlight GUI
Notes:
Currently renderer.shadowMap.type is not set to THREE.PCFSoftShadowMap for better performance.
End result:
Click here to see the end result.
Assignment's GitHub page.


838_diana_334.py
class Solution:
def countRestrictedPaths(self, n: int, edges: List[List[int]]) -> int:
distances = dict()
heap = []
graph = collections.defaultdict(list)
for u, v, w in edges:
graph[u].append((v, w))
graph[v].append((u, w))
heap = [(0, n)] # (dis, node)
while heap:
dis, node = heapq.heappop(heap)
if node in distances:
continue
distances[node] = dis
for nnode, weight in graph[node]:
if nnode not in distances:
heapq.heappush(heap, (dis + weight, nnode))
# print(distances)
MOD = 10**9 + 7
@functools.lru_cache(n)
def dp(node):
if node == 1:
return 1
count = 0
for nnode, _ in graph[node]:
if distances[node] < distances[nnode]:
count += dp(nnode)
return count
return dp(n) % MOD
# def dfs(node):
# nonlocal count
# if node == n:
# count += 1
# return
# for nnode, _ in graph[node]:
# if distances[node] > distances[nnode]:
# dfs(nnode)
# dfs(1)
# return count % MOD


This is just a quick litte calculator to automate the design of difference amplifier circuits - as they relate to high side current sense applications.


| Xmega Application Note | |||||
Data Structures | |
| struct | pll_config |
| Hardware-specific representation of PLL configuration. More... | |
Defines | |
| #define | NR_PLLS 1 |
| #define | NR_PLLS 2 |
| #define | pll_config_defaults(cfg, pll_id) |
| #define | pll_config_defaults(cfg, pll_id) |
| #define | pll_get_default_rate(pll_id) |
| #define | pll_get_default_rate(pll_id) |
| #define | PLL_MAX_HZ 200000000UL |
| #define | PLL_MAX_HZ 240000000 |
| #define | PLL_MIN_HZ 10000000UL |
| #define | PLL_MIN_HZ 40000000 |
| #define | PLL_NR_OPTIONS 0 |
| #define | PLL_TIMEOUT_MS div_ceil(1000 * (PLL_MAX_STARTUP_CYCLES * 2), OSC_SLOW_MIN_HZ) |
| Number of milliseconds to wait for PLL lock. | |
Enumerations | |
| enum | pll_source { PLL_SRC_OSC0 = 0, PLL_SRC_OSC1 = 1, PLL_NR_SOURCES, PLL_SRC_RC2MHZ = OSC_PLLSRC_RC2M_gc, PLL_SRC_RC32MHZ = OSC_PLLSRC_RC32M_gc, PLL_SRC_XOSC = OSC_PLLSRC_XOSC_gc } |
| enum | pll_source { PLL_SRC_OSC0 = 0, PLL_SRC_OSC1 = 1, PLL_NR_SOURCES, PLL_SRC_RC2MHZ = OSC_PLLSRC_RC2M_gc, PLL_SRC_RC32MHZ = OSC_PLLSRC_RC32M_gc, PLL_SRC_XOSC = OSC_PLLSRC_XOSC_gc } |
PLL clock source. More... | |
Functions | |
| static uint32_t | pll_get_default_rate_priv (enum pll_source src, unsigned int mul, unsigned int div) |
| Return clock rate for specified PLL settings. | |
PLL configuration | |
|
| |
| static void | pll_config_clear_option (struct pll_config *cfg, unsigned int option) |
| Clear the PLL option bit option in the configuration cfg. | |
| static void | pll_config_init (struct pll_config *cfg, enum pll_source src, unsigned int div, unsigned int mul) |
| Initialize PLL configuration from standard parameters. | |
| static void | pll_config_read (struct pll_config *cfg, unsigned int pll_id) |
| Read the currently active configuration of pll_id. | |
| static void | pll_config_set_option (struct pll_config *cfg, unsigned int option) |
| Set the PLL option bit option in the configuration cfg. | |
| static void | pll_config_write (const struct pll_config *cfg, unsigned int pll_id) |
| Activate the configuration cfg on pll_id. | |
Interaction with the PLL hardware | |
|
| |
| static void | pll_disable (unsigned int pll_id) |
| Disable the PLL identified by pll_id. | |
| static void | pll_enable (const struct pll_config *cfg, unsigned int pll_id) |
| Activate the configuration cfg and enable PLL pll_id. | |
| static bool | pll_is_locked (unsigned int pll_id) |
| Determine whether the PLL is locked or not. | |
| static int | pll_wait_for_lock (unsigned int pll_id) |
| Wait for PLL pll_id to become locked. | |
Chip-specific PLL characteristics | |
|
| |
| #define | PLL_MAX_STARTUP_CYCLES ((1 << AVR32_PM_PLL0_PLLCOUNT_SIZE) - 1) |
| Maximum PLL startup time in number of slow clock cycles. | |
Chip-specific PLL options | |
|
| |
| #define | PLL_NR_OPTIONS AVR32_PM_PLL0_PLLOPT_SIZE |
| Number of PLL options. | |
| #define | PLL_OPT_OUTPUT_DIV 1 |
| Divide output frequency by two. | |
| #define | PLL_OPT_VCO_RANGE_LOW 0 |
| VCO frequency range is 80-180 MHz (160-240 MHz if unset). | |
| #define | PLL_OPT_WBM_DISABLE 2 |
| Disable wide-bandwidth mode. | |
| #define | PLL_VCO_LOW_THRESHOLD AVR32_PM_PLL_VCO_RANGE0_MIN_FREQ |
| The threshold under which to set the PLL_OPT_VCO_RANGE_LOW option. |
This group contains functions and definitions related to configuring and enabling/disabling on-chip PLLs. A PLL will take an input signal (the source), optionally divide the frequency by a configurable divider, and then multiply the frequency by a configurable multiplier.
Some devices don't support input dividers; specifying any other divisor than 1 on these devices will result in an assertion failure. Other devices may have various restrictions to the frequency range of the input and output signals.
The following example shows how to configure and enable PLL0 using the default parameters specified using the configuration symbols listed above, and with Wide Bandwidth Mode disabled (a UC3A3-specific PLL option.)
struct pll_config pllcfg; pll_config_defaults(&pllcfg, 0); pll_config_set_option(&pllcfg, PLL_OPT_WBM_DISABLE); pll_enable(&pllcfg, 0); pll_wait_for_lock(0);
When the last function call returns, PLL0 is ready to be used as the main system clock source.
Each PLL has a set of default parameters determined by the following configuration symbols in the application's configuration file:
These configuration symbols determine the result of calling pll_config_defaults() and pll_get_default_rate().
| #define NR_PLLS 2 |
Definition at line 47 of file pll.h.
Referenced by pll_config_read(), pll_config_write(), pll_disable(), pll_enable(), pll_is_locked(), and pll_wait_for_lock().
| #define pll_config_defaults | ( | cfg, | |||
| pll_id | ) |
pll_config_init(cfg, \ CONFIG_PLL##pll_id##_SOURCE, \ CONFIG_PLL##pll_id##_DIV, \ CONFIG_PLL##pll_id##_MUL)
| #define pll_config_defaults | ( | cfg, | |||
| pll_id | ) |
pll_config_init(cfg, \ CONFIG_PLL##pll_id##_SOURCE, \ CONFIG_PLL##pll_id##_DIV, \ CONFIG_PLL##pll_id##_MUL)
| #define pll_get_default_rate | ( | pll_id | ) |
pll_get_default_rate_priv(CONFIG_PLL##pll_id##_SOURCE, \ CONFIG_PLL##pll_id##_MUL, \ CONFIG_PLL##pll_id##_DIV)
| #define pll_get_default_rate | ( | pll_id | ) |
((osc_get_rate(CONFIG_PLL##pll_id##_SOURCE) \ * CONFIG_PLL##pll_id##_MUL) \ / CONFIG_PLL##pll_id##_DIV)
Definition at line 93 of file pll.h.
Referenced by sysclk_get_main_hz().
| #define PLL_MAX_HZ 240000000 |
Definition at line 60 of file pll.h.
Referenced by pll_get_default_rate_priv().
| #define PLL_MAX_STARTUP_CYCLES ((1 << AVR32_PM_PLL0_PLLCOUNT_SIZE) - 1) |
| #define PLL_MIN_HZ 40000000 |
Definition at line 59 of file pll.h.
Referenced by pll_get_default_rate_priv().
| #define PLL_NR_OPTIONS AVR32_PM_PLL0_PLLOPT_SIZE |
Number of PLL options.
Definition at line 71 of file pll.h.
Referenced by pll_config_clear_option(), and pll_config_set_option().
| #define PLL_OPT_VCO_RANGE_LOW 0 |
| #define PLL_TIMEOUT_MS div_ceil(1000 * (PLL_MAX_STARTUP_CYCLES * 2), OSC_SLOW_MIN_HZ) |
| #define PLL_VCO_LOW_THRESHOLD AVR32_PM_PLL_VCO_RANGE0_MIN_FREQ |
The threshold under which to set the PLL_OPT_VCO_RANGE_LOW option.
| enum pll_source |
Definition at line 53 of file pll.h.
00053 { 00055 PLL_SRC_RC2MHZ = OSC_PLLSRC_RC2M_gc, 00057 PLL_SRC_RC32MHZ = OSC_PLLSRC_RC32M_gc, 00059 PLL_SRC_XOSC = OSC_PLLSRC_XOSC_gc, 00060 };
| enum pll_source |
PLL clock source.
Definition at line 83 of file pll.h.
00083 { 00084 PLL_SRC_OSC0 = 0, 00085 PLL_SRC_OSC1 = 1, 00086 PLL_NR_SOURCES, 00087 };
| void pll_config_clear_option | ( | struct pll_config * | cfg, | |
| unsigned int | option | |||
| ) | [inline, static] |
Clear the PLL option bit option in the configuration cfg.
| cfg | The PLL configuration to be changed. | |
| option | The PLL option bit to be cleared. |
Definition at line 106 of file pll.h.
References Assert, pll_config::ctrl, and PLL_NR_OPTIONS.
00108 { 00109 Assert(option < PLL_NR_OPTIONS); 00110 00111 cfg->ctrl &= ~(1U << (AVR32_PM_PLL0_PLLOPT + option)); 00112 }
| static void pll_config_init | ( | struct pll_config * | cfg, | |
| enum pll_source | src, | |||
| unsigned int | div, | |||
| unsigned int | mul | |||
| ) | [inline, static] |
Initialize PLL configuration from standard parameters.
The PLL options PLL_OPT_VCO_RANGE_LOW and PLL_OPT_OUTPUT_DIV will be set automatically based on the calculated target frequency.
| cfg | The PLL configuration to be initialized. | |
| src | The oscillator to be used as input to the PLL. | |
| div | PLL input divider. | |
| mul | PLL loop divider (i.e. multiplier). |
Definition at line 126 of file pll.h.
References Assert, pll_config::ctrl, and PLL_SRC_RC32MHZ.
00128 { 00129 Assert(mul >= 1 && mul <= 31); 00130 00131 if (src == PLL_SRC_RC32MHZ) { 00132 Assert(div == 4); 00133 } else { 00134 Assert(div == 1); 00135 } 00136 00137 /* Initialize the configuration */ 00138 cfg->ctrl = src | (mul << OSC_PLLFAC_gp); 00139 }
| static void pll_config_read | ( | struct pll_config * | cfg, | |
| unsigned int | pll_id | |||
| ) | [inline, static] |
Read the currently active configuration of pll_id.
| cfg | The configuration object into which to store the currently active configuration. | |
| pll_id | The ID of the PLL to be accessed. |
Definition at line 147 of file pll.h.
References Assert, pll_config::ctrl, and NR_PLLS.
| void pll_config_set_option | ( | struct pll_config * | cfg, | |
| unsigned int | option | |||
| ) | [inline, static] |
Set the PLL option bit option in the configuration cfg.
| cfg | The PLL configuration to be changed. | |
| option | The PLL option bit to be set. |
Definition at line 98 of file pll.h.
References Assert, pll_config::ctrl, and PLL_NR_OPTIONS.
00100 { 00101 Assert(option < PLL_NR_OPTIONS); 00102 00103 cfg->ctrl |= 1U << (AVR32_PM_PLL0_PLLOPT + option); 00104 }
| static void pll_config_write | ( | const struct pll_config * | cfg, | |
| unsigned int | pll_id | |||
| ) | [inline, static] |
Activate the configuration cfg on pll_id.
| cfg | The configuration object representing the PLL configuration to be activated. | |
| pll_id | The ID of the PLL to be updated. |
Definition at line 154 of file pll.h.
References Assert, pll_config::ctrl, and NR_PLLS.
Referenced by pll_enable().
| static void pll_disable | ( | unsigned int | pll_id | ) | [inline, static] |
Disable the PLL identified by pll_id.
After this function is called, the PLL identified by pll_id will be disabled. The PLL configuration stored in hardware may be affected by this, so if the caller needs to restore the same configuration later, it should either do a pll_config_read() before disabling the PLL, or remember the last configuration written to the PLL.
| pll_id | The ID of the PLL to be disabled. |
Definition at line 183 of file pll.h.
References Assert, cpu_irq_restore(), cpu_irq_save(), and NR_PLLS.
00184 { 00185 irqflags_t flags; 00186 00187 Assert(pll_id < NR_PLLS); 00188 00189 flags = cpu_irq_save(); 00190 OSC.CTRL &= ~OSC_PLLEN_bm; 00191 cpu_irq_restore(flags); 00192 }
| static void pll_enable | ( | const struct pll_config * | cfg, | |
| unsigned int | pll_id | |||
| ) | [inline, static] |
Activate the configuration cfg and enable PLL pll_id.
| cfg | The PLL configuration to be activated. | |
| pll_id | The ID of the PLL to be enabled. |
Definition at line 167 of file pll.h.
References Assert, cpu_irq_restore(), cpu_irq_save(), NR_PLLS, and pll_config_write().
00169 { 00170 irqflags_t flags; 00171 00172 Assert(pll_id < NR_PLLS); 00173 00174 flags = cpu_irq_save(); 00175 pll_config_write(cfg, pll_id); 00176 OSC.CTRL |= OSC_PLLEN_bm; 00177 cpu_irq_restore(flags); 00178 }
| static uint32_t pll_get_default_rate_priv | ( | enum pll_source | src, | |
| unsigned int | mul, | |||
| unsigned int | div | |||
| ) | [inline, static] |
Return clock rate for specified PLL settings.
For internal use only.
| src | ID of the PLL's reference source oscillator. | |
| mul | Multiplier for the PLL. | |
| div | Divisor for the PLL. |
| Output | clock rate from PLL. |
Definition at line 81 of file pll.h.
References Assert, osc_get_rate(), OSC_ID_XOSC, PLL_MAX_HZ, PLL_MIN_HZ, PLL_SRC_RC2MHZ, PLL_SRC_RC32MHZ, and PLL_SRC_XOSC.
00083 { 00084 uint32_t rate; 00085 00086 switch (src) { 00087 case PLL_SRC_RC2MHZ: 00088 rate = 2000000UL; 00089 Assert(div == 1); 00090 break; 00091 00092 case PLL_SRC_RC32MHZ: 00093 rate = 8000000UL; 00094 Assert(div == 4); 00095 break; 00096 00097 case PLL_SRC_XOSC: 00098 rate = osc_get_rate(OSC_ID_XOSC); 00099 Assert(div == 1); 00100 break; 00101 00102 default: 00103 break; 00104 } 00105 00106 Assert(rate >= 440000UL); 00107 00108 rate *= mul; 00109 00110 Assert(rate >= PLL_MIN_HZ); 00111 Assert(rate <= PLL_MAX_HZ); 00112 00113 return rate; 00114 }
| static bool pll_is_locked | ( | unsigned int | pll_id | ) | [inline, static] |
Determine whether the PLL is locked or not.
| pll_id | The ID of the PLL to check. |
| true | The PLL is locked and ready to use as a clock source | |
| false | The PLL is not yet locked, or has not been enabled. |
Definition at line 194 of file pll.h.
References Assert, and NR_PLLS.
Referenced by pll_wait_for_lock().
| static int pll_wait_for_lock | ( | unsigned int | pll_id | ) | [inline, static] |
Wait for PLL pll_id to become locked.
| pll_id | The ID of the PLL to wait for. |
| STATUS_OK | The PLL is now locked. | |
| ERR_TIMEOUT | Timed out waiting for PLL to become locked. |
Definition at line 261 of file pll.h.
References Assert, NR_PLLS, and pll_is_locked().
00262 { 00263 Assert(pll_id < NR_PLLS); 00264 00265 while (!pll_is_locked(pll_id)) { 00266 /* Do nothing */ 00267 } 00268 00269 return 0; 00270 }
| Generated on Fri Oct 22 12:15:26 2010 for AVR1300 Using the Xmega ADC by 1.6.3 |


Are you sure you want to set tomorrow's menu? You will not be able to change the menu after this. The driver will pick up the food at the specified time.
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sayIt adapter settings
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--- layout: default title: SSD ---
The SSD model is implemented in one file `ssd.py` on PyTorch.
Details: https://github.com/iamhankai/ssd-pytorch.


Firmware Release Information
Version 2.8.0
The ICM now supports remote access from www.myEcoConcierge.com.
This website makes it very simple to enable remote access to ICMs,
with no need to use static IP addressing, forward router ports or compromise the local firewall.
Note: Reset the ICM to factory defaults after the upgrade to enable all the new features.
Upgrade Information
Upgrade Instructions
- Enter http://myhome/setup in the address bar of the web browser.
- Click Advanced in the navigation bar on the left side of the screen, then click Upgrade.
- Select the Node to upgrade from the list, then click AutoUpgrade.
- At the end of the upgrade process, select Reset to factory defaults to enable all the new features.
This will require you to reconfigure the ICM.
- Make sure you upgrade all the ICMs on the network to the same firmware version.
Note: If the upgrade fails, verify your Internet connection. Wait 5 minutes, and then restart the ICM. Try the upgrade process again.
Revision History
2.8.0
- Improved device management, time management, and setup.
- Vizia ICM - New ICM controls Leviton ViziaRF lighting systems.
- Energy ICM - Added humidity alerts and improved EnviraCom error handling.
2.7.2
- Added trouble-free remote access support and event logging through www.in2myhome.com
- Improved support for corporate networks and static IP addressing.
- Reset button handling works more reliably.
- Email reliability is improved with a trusted email server.
- Local web page improvements reduce Java errors.
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- New daylight savings time schedule is supported.
- Added a software watchdog to allow the ICM to recover from catastrophic errors.
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log_message
, log_warning
, and log_error
are
wrapper functions for logging events of type
MESSAGE
, WARNING
, or ERROR
.
Relevant message, warning or error is raised after logging an event.
Raising an error is done using stop
function
and it can stop the whole shiny app.
log_message(...) log_warning(...) log_error(...)
| ... | Objects that are evaluated, coerced into character string, collapsed and pasted as event name into log entry header. The character string is also passed to the message, warning, or error raised. |
|---|
log_message
: Logging a message
log_warning
: Logging a warning
log_error
: Logging an error
Other logging events functions: log_event
,
log_output
, log_started
,
log_test
, log_value
if (interactive()) { set_logging() shiny::shinyApp( ui = shiny::fluidPage(log_init()), server = function(input, output) { set_logging_session() log_message("Example of a message") log_warning("Example of a warning") log_error("Example of an error") } ) }


onesubwiper is a small script to let you overwrite your comments on certain subreddits. It works on Linux and Mac OS X, you will need to have python and curl installed.
download onesubwiper
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Step 2. copy authorization code


4 references to SupportsWildcardsAttribute
System.Management.Automation (4)
engine\parser\TypeResolver.cs (1)
775
{ typeof(
SupportsWildcardsAttribute
), new[] { "SupportsWildcards" } },
help\DefaultCommandHelpObjectBuilder.cs (1)
533
if (attrib is
SupportsWildcardsAttribute
)
help\HelpCommentsParser.cs (2)
291
bool supportsWildcards = compiledAttributes.OfType<
SupportsWildcardsAttribute
>().Any();
485
bool supportsWildcards = parameter.CompiledAttributes.Any(static attribute => attribute is
SupportsWildcardsAttribute
);


MMOCP (Massively Multiplayer Online Color Picker). Source.Pick a color up top to add it to the stream. #Users: {{> howmany}}


It is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer
to generate Lorem Ipsum which looks reasonable. The generated Lorem Ipsum is therefore always free from repetition, injected humour, or non-characteristic words


2. Getting Started¶
Note
The SEAD Light will henceforth be referred to as a “Device” for sake of generalizing. This framework was built with the SEAD Light in mind, however any type of “device” is compatible if they follow the API Guidelines.
At this point, the project is now ready to begin accepting clients and devices.
To get devices connected to your project, simply point their database address at the server that faces the internet (for the scalable framework, this is the stateless server or load balancer). This will cause the devices to attempt to find themselves in the server, fail, then register themselves in the system.
From here, users can register to the website and pair to devices via their serial number.
2.1. API Documentation¶
You can interact with the server’s API by navigating to /docs. This interface was designed to allow developers easy access to the framework so that they can learn it rapidly and integrate new types of devices into the database.
The REST API is set up to be very trusting. There are no checks for malicious behavior and we assume all users are benign. It is possible to alter any and all properties of a device such as changing the owner via the API. In the future, it is recommended to include API token authentication to prevent malicious attacks.
2.2. Administrative Interface¶
In addition to the front facing web interface, there is an administrative interface for managing database models directly. This interface was designed to allow an administrator the ability to alter the way the website functions without having to interface with the source code directly.
An administrator can use this interface to create/modify devices, circuit types, and appliance directly. This would presumably become useful when the algorithms on the SEAD Light mature to the point where disaggregation by appliance is feasable.
In addition, an administrator can also interface with the facets of the web application, including how event notifications are handled as well as add/modify interval notifications. These are the emails sent to users after a certain event has been detected or an interval has elapsed.
There is the ability to add/modify rate plans, territories, and utility companies to the web application, giving more realistic cost predictions for a user’s device.
2.3. Connecting Devices to the Framework¶
With the new framework in place, we are now ready to begin connecting devices to the database. The general outline on how this is done is as follows:
- Point device at the API Endpoint for your framework.
The endpoint of your system depends on whether or not the scalable framework is in place. If it is, then the endpoint is the address of the Load Balancer. If this is an atomic installation, then the endpoint is the address of the machine running the applications.
For the API already in place, this would be:
http://seads.io/api/
Query the devices database to check if the serial of the device is registered:
GET http://seads.io/api/device-api/{serial}/
If the response is 404 (not found), continue to the next step. If the response is 302 (found), then the device is already connected and ready to transmit.
Register the device with the framework:
POST http://seads.io/api/device-api/?serial={serial}
This POST request will register the device within the server, allocating database series and instantiating web application models that will relate to this device.
At this point, the device is ready to start transmitting packets. The device can transmit packets to the server without an owner. It is assumed that the owner is interested in seeing all data even before the device is paired, so the data is stored regardless of if the device is an orphan or not.
Begin data transmission:
POST http://seads.io/api/device-api/ { "device": "/api/device-api/{serial}/", "time" : ["0x14d95894815", "0x1"], "dataPoints":[ {"wattage": 170}, {"wattage": 169}, ... ] }
This is a typical packet that could be sent to the server from the device. Here is a breakdown of the fields:
- Device: The hyperlinked device sending the data.
- Time: A tuple of the format [start_time, period] in hexadecimal describing the packet’s timing in milliseconds.
- dataPoints: An array of undefined size containing measured values for the server. The data points are numbered 0 ... n ... j where nth data point has a timestamp of start_time + n * period and j is undefined.


The relationship between lack of healthcare, percentage of smokers, and obesity percentage is compared to poverty percentage, median age and median household income for all states in the U.S.
These correlations can be visualized by the graph. For exmaple, when comparing lack of healthcare to poverty, the data is spread across the graph, meaning that there is no direct relationship between percentage of poverty and percentage of people lacking healthcare. This can be attributed to Medicaid and Medicare programs that are available for people with limited resources. Similarly, there is no direct correlation between median household income and percentage of people lacking healthcare. This can be attributed to people with higher incomes being able to afford commercial health insurance policies. On the other hand, for most states, people who lack healthcare are within the age range of 36 - 40.
Percentage of smokers and percentage of obesity show similar results. There is no direct correlation between percentage of smokers, or obesity percentage, and the percentage of poverty and/or median household income. Also, those states with the highest percentage of smokers (or obesity percentage), have the highest percentage of poverty and the lowest median household income. Again, for most states, people who smoke (or those considered obese) are within the age range of 36 - 40.
In conclusion, people in states with a higher poverty percentage (or lower household income) are more prone to obesity and smoking, even though healthcare is accessible across most states and most household incomes. However, in most states, health risks such as obesity, smoking and lack of healthcare are higher for people within the age range of 36-40.


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Uber CEO Comment on Khashoggi Murder Sparks Revulsion
by ASSOCIATED PRESSNEW YORK (AP) — Uber CEO Dara Khosrowshahi is being criticized for calling the murder of Washington Post columnist Jamal Khashoggi “a mistake” and comparing it to the death of a pedestrian struck by one of his company’s autonomous vehicles.
Khosrowshahi later said he regretted his comments, made during an interview with Axios on HBO. He tweeted Monday that there’s no forgiving or forgetting what happened to Khashoggi and he was wrong to call it a mistake.
Critics say Khosrowshahi is downplaying Khashoggi’s grisly murder to placate one of the company’s biggest investors.
Saudi Arabia’s sovereign wealth fund, known as The Public Investment Fund, holds about $1.9 billion worth of Uber stock, making it the company’s fifth-largest stakeholder. Its managing director, Yasir Al-Rumayyan, sits on Uber’s board.
U.S. and United Nations officials say that Saudi Arabian Prince Mohammed bin Salman played a role in Khashoggi’s slaying. Prince Mohammed has said he takes responsibility but denied ordering the killing, calling the slaying “a mistake” in an interview in September.
In an interview with Axios broadcast Sunday, Khosrowshahi echoed those comments, saying “I think that government said that they made a mistake.” He then compared Khashoggi’s slaying to an accident in which one of Uber’s autonomous vehicles struck and killed a pedestrian last year in Arizona.
“It’s a serious mistake. We’ve made mistakes too, with self-driving, and we stopped driving and we’re recovering from that mistake,” Khosrowshahi said. “So I think that people make mistakes; it doesn’t mean that they can never be forgiven. I think they’ve taken it seriously.”
Khashoggi was killed and dismembered by Saudi intelligence officials and a doctor last year at the Saudi Consulate in Istanbul. He was a longtime editor at state-linked newspapers in Saudi Arabia and had been in self-imposed exile in the United States, writing critically about Saudi leadership.
A U.N. investigator said he was the victim of “a planned, organized, well-resourced and premeditated extrajudicial killing for which the state of Saudi Arabia must bear responsibility.”
In his Monday tweet, Khosrowshahi said he told Axios after the interview, “I said something in the moment I don’t believe. Our investors have long known my views here & I’m sorry I wasn’t as clear on Axios.”
Nonetheless, #BoycottUber began trending on Twitter Monday, recalling the #DeleteUber movement that gathered steam years ago as the company struggled with image problems and lost customers to rival Lyft.
Khosrowshahi showed terrible judgment as a leader, said Paul Argenti, professor of corporate communications at Dartmouth’s Tuck School of Business.
“If I were a board member there, I would really be wondering about this guy,” Argenti said. “Uber has enough problems without him. This is just another nail in the coffin.”
It was perhaps the first public relations disaster Khosrowshahi brought upon himself after joining Uber as CEO to turn around a company that has been plagued with self-inflicted wounds.
Khosrowshahi has largely managed to stay out of the spotlight, unlike Uber’s co-founder Travis Kalanick, who was ousted as CEO in 2017 after revelations about rampant sexual harassment, accusations that drivers had assaulted passengers and a cover-up of a computer break-in that stole personal information about passengers, among other problems.
But especially in markets where there are few alternatives to Uber, consumers are unlikely to abandon the service over Khosrowshahi’s remarks, said Rosalind Chow, associate professor of organizational behavior and theory at Carnegie Mellon University.
“People have very short-term memories,” she said.
Copyright © 2019 courthousenews.com


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As previously noted, by default transaction commits are durable because they cause the modifications performed under the transaction to be synchronously recorded in your on-disk log files. However, it is possible to use non-durable transactions.
You may want non-durable transactions for performance reasons. For example, you might be using transactions simply for the isolation guarantee. In this case, you might want to relax the synchronized write to disk that JE normally performs as part of a transaction commit. Doing so means that your data will still make it to disk; however, your application will not necessarily have to wait for the disk I/O to complete before it can perform another database operation. This can greatly improve throughput for some workloads.
To relax the durability guarantee for your transactions,
you use the Durability
class to
define the durability policy that you want to use. The
Durability
class constructor takes
three arguments, only one of which is interesting for a
standalone transactional application:
The synchronization policy for the local machine.
The synchronization policy for Replicas. Used only for JE HA applications.
The acknowledgement policy. Again, this is required only for JE HA applications.
We describe JE High Availability Applications in the Berkeley DB, Java Edition Getting Started with High Availability Applications guide.
The synchronization policy that you give the
Durability
class constructor can be
one of the following:
Durability.SyncPolicy.SYNC
Write and synchronously flush the log to disk upon transaction commit. This offers the most durable transaction configuration because the commit operation will not return until all of the disk I/O is complete. But, conversely, this offers the worse possible write performance because disk I/O is an expensive and time-consuming operation.
This is the default synchronization policy. A transaction that uses this policy is considered to be durable.
Durability.SyncPolicy.NO_SYNC
This causes JE to not synchronously force any data to disk upon transaction commit. That is, the modifications are held entirely inside the JVM and the modifications are not forced to the file system for long-term storage. Note, however, that the data will eventually make it to the filesystem (assuming no application or OS crashes) as a part of JE's management of its logging buffers and/or cache.
This form of a commit provides a weak durability guarantee because data loss can occur due to an application, JVM, or OS crash. In fact, this represents the least durable configuration that you can provide for your transactions. But it also offers much better write performance than the other options.
Durability.SyncPolicy.WRITE_NO_SYNC
This causes data to be synchronously written to the OS's file system buffers upon transaction commit. The data will eventually be written to disk, but this occurs when the operating system chooses to schedule the activity; the transaction commit can complete successfully before this disk I/O is performed by the OS.
This form of commit protects you against application and JVM crashes, but not against OS crashes. This method offers less room for the possibility of data loss than does NO_SYNC.
You can specify your durability policy on an
environment-wide basis by creating a
Durability
class and then giving it
to EnvironmentConfig.setDurability()
.
You can also override the environment default durability
policy on a transaction-by-transaction basis by providing a
Durability
class to the
TransactionConfig
object you use to
configure your transaction using the
TransactionConfig.setDurability()
method.
For example:
package je.txn; import com.sleepycat.je.Database; import com.sleepycat.je.DatabaseConfig; import com.sleepycat.je.DatabaseEntry; import com.sleepycat.je.DatabaseException;import com.sleepycat.je.Durability;
import com.sleepycat.je.Environment; import com.sleepycat.je.EnvironmentConfig; import com.sleepycat.je.Transaction;import com.sleepycat.je.TransactionConfig;
import java.io.File; ... Database myDatabase = null; Environment myEnv = null; try {Durability defaultDurability = new Durability(Durability.SyncPolicy.NO_SYNC, null, // unused by non-HA applications. null); // unused by non-HA applications.
EnvironmentConfig myEnvConfig = new EnvironmentConfig(); myEnvConfig.setTransactional(true);myEnvConfig.setDurability(defaultDurability);
myEnv = new Environment(new File("/my/env/home"), myEnvConfig); // Open the database. Create it if it does not already exist. DatabaseConfig dbConfig = new DatabaseConfig(); dbConfig.setTransactional(true); myDatabase = myEnv.openDatabase(null, "sampleDatabase", dbConfig); String keyString = "thekey"; String dataString = "thedata"; DatabaseEntry key = new DatabaseEntry(keyString.getBytes("UTF-8")); DatabaseEntry data = new DatabaseEntry(dataString.getBytes("UTF-8"));Durability newDurability = new Durability(Durability.SyncPolicy.WRITE_NO_SYNC, null, // unused by non-HA applications. null); // unused by non-HA applications. TransactionConfig tc = new TransactionConfig(); tc.setDurability(newDurability); Transaction txn = myEnv.beginTransaction(null, tc);
try { myDatabase.put(txn, key, data); txn.commit(); } catch (Exception e) { if (txn != null) { txn.abort(); txn = null; } } } catch (DatabaseException de) { // Exception handling goes here }


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class: center, middle, title-slide count: false # Lecture 1: ### Machine Learning pipeline ### ... and an overview of this course
.bold[Marc Lelarge] --- # Goal of the course ## Overview - Learn the basics of deep learning: "How" it works - Learn a deep learning framework: [PyTorch](https://pytorch.org/) - See various application domains: vision, recommender systems, NLP... -- ## Deep Learning .red[Do It Yourself!] - Fast evolving field with a lot of ressources available online - Use a neural network right away, get results and then understand it! - .red[This course requires your active participation] -- ## Course website: [Basics of Deep Learning](https://mlelarge.github.io/dataflowr-web/plutonai.html) --- # What is Deep Learning ### Typical supervised machine learning system .center[
] --- # What is Deep Learning ### Typical supervised machine learning system .center[
] --- # What is Deep Learning ### Typical supervised machine learning system .center[
] --- # What is Deep Learning ### Deep Learning system .center[
] .credit[Credit: image of features [Zeiler, Fergus 2014](https://cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)] --- # Why Deep Learning Now? - Five decades of research in machine learning - .grey[CPUs/GPUs/storage developed for other purposes] - .grey[lots of data from “the internet”] - .grey[tools and culture of collaborative and reproducible science] - .grey[resources and efforts from large corporations] --- # Why Deep Learning Now? - Five decades of research in machine learning - CPUs/GPUs/storage developed for other purposes - .grey[lots of data from “the internet”] - .grey[tools and culture of collaborative and reproducible science] - .grey[resources and efforts from large corporations] .center[
_GPU and TPU_
] --- # Why Deep Learning Now? - Five decades of research in machine learning - CPUs/GPUs/storage developed for other purposes - lots of data from “the internet” - .grey[tools and culture of collaborative and reproducible science] - .grey[resources and efforts from large corporations] .center[
] --- # Why Deep Learning Now? - Five decades of research in machine learning - CPUs/GPUs/storage developed for other purposes - lots of data from “the internet” - tools and culture of collaborative and reproducible science - resources and efforts from large corporations .center[
] --- # Deep Learning pipeline ## Dataset and Dataloader .center[
] --- # Deep Learning pipeline ## Model .center[
] --- # Deep Learning pipeline ## Loss and Optimizer .center[
] --- # Deep Learning pipeline ## Dataset and Dataloader + Model + Loss and Optimizer = Training .center[
] --- # Overview of the course: 1- Course overview: machine learning pipeline --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation * .red[Training] - in PyTorch: [`torch.Tensor`](https://pytorch.org/docs/stable/tensors.html) and [`torch.autograd`](https://pytorch.org/docs/stable/autograd.html) --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation 3- Classification with deep learning * .red[Loss] and .red[Optimizer] - in PyTorch: [`torch.optim`](https://pytorch.org/docs/stable/optim.html) and [Loss functions](https://pytorch.org/docs/stable/nn.html#loss-functions) in `torch.nn` --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation 3- Classification with deep learning 4- Convolutional neural networks * .red[Building block for a model] - in PyTorch: [Convolutional layrers](https://pytorch.org/docs/stable/nn.html#convolution-layers) and [Pooling layers](https://pytorch.org/docs/stable/nn.html#pooling-layers) in `torch.nn` --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation 3- Classification with deep learning 4- Convolutional neural networks 5- Embedding layers and dataloaders * .red[Building block for a model] - in Pytorch: [Sparse layers](https://pytorch.org/docs/stable/nn.html#sparse-layers) * .red[Datasets and dataloaders] - in PyTorch: [`torch.utils.data`](https://pytorch.org/docs/stable/data.html) --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation 3- Classification with deep learning 4- Convolutional neural networks 5- Embedding layers and dataloaders 6- Unsupervised learning: auto-encoders and generative adversarial networks * .red[Deep learning architectures] - in PyTorch: recap! --- # Overview of the course: 1- .grey[Course overview: machine learning pipeline] 2- PyTorch tensors and automatic differentiation 3- Classification with deep learning 4- Convolutional neural networks 5- Embedding layers and dataloaders 6- Unsupervised learning: auto-encoders and generative adversarial networks 7- Recurrent neural networks * .red[Deep learning architectures] - in Pytorch: [Recurrent layers](https://pytorch.org/docs/stable/nn.html#recurrent-layers) --- # Organization of the course ## Each module is made of: ### a video ### some slides (presented in the video) ### a jupyter notebook (presented in the video) ### some practicals -- ## All the ressources are available through [Pluton AI](https://pluton.ai/course) -- # And now a [first example](https://colab.research.google.com/github/mlelarge/dataflowr/blob/master/PlutonAI/01_intro_PlutonAI_colab.ipynb)! --- class: end-slide, center count: false The end.


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500 Writing Prompts for Kids: First Grade through Fifth Grade.
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Creative Writing. Prices. Back to School Activities for 1st Grade: This poetry unit for first and second grade gets students writing both form and free-verse. Second Grade Writing Activities. Check out these writing activities for 2nd grade! Kids will love these creative ways to diagram plots, differentiate. Creative writing activities for second grade. Creative writing prompts for 2nd graders And second-grade writing prompts that will be able to see common core standards. Free First Grade Worksheets:Reading, Phonics, Rhyming.
2nd Grade Creative Writing Activities Teaching Resources. Also known as 'the art of making things up,' creative writing is a vital part of modern society. This lesson will explore what makes writing. Creative writing activities for second grade - Palazzo. Sight word games and activities for beginning readers. Tons of great activities for sight words in kindergarten and first grade. Doing activities with your children allows you to promote their reading and writing skills while having fun at the same time. These activities for pre-readers. Creative Writing Resources for Teachers (K-12) - TeacherVision.
Creative writing activities for second grade - 100% non-plagiarism guarantee of custom essays papers. Quick and trustworthy writings from industry top agency. begin. Second Grade Curriculum 2nd Grade Lessons. 2nd Grade Writing Activities for Kids Education.com. Build solid creative writing skills with our extensive collection of printables, graphic organizers, and lessons plans. You ll find poetry activities, short-story. Sight Word Activities-Sparkling in Second.  What is Creative Writing? - Definition, Types Examples.


{% extends "innerbase.html" %} {% import "bootstrap/wtf.html" as wtf %} {% block title %}Time-Line{% endblock %} {% block scripts %} {{ super() }} {% endblock %} {% block timeshow %} {{ mm.timemacro(birth) }} {% endblock %} {% block page_content %}
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WITNESS LIST State Affairs Committee March 4, 1999-12:30P SB 57 For: Schneider, Rob (Consumers Union), Austin Walter, Paul (FOI Foundation of Texas), Dallas Registering, but not testifying: For: Hayes, Patricia (Texas Classroom Teachers Association), Austin Pederson, Tony (Texas Daily Newspaper Association & Texas Press Association), Austin Schneider, Michael (Texas Association of Broadcasters), Austin Smith, Tom "Smitty" (Public Citizen), Austin SB 183 Registering, but not testifying: On: Steele, Penny (Texas Department of Mental Health & Mental Retardation), Austin SB 277 For: Pederson, Tony (Texas Daily Newspaper Association & Texas Press Association), Houston Schneider, Rob (Consumers Union), Austin On: Payne, Rebecca L. (Office of the Attorney General), Austin Registering, but not testifying: For: Hayes, Patricia (Texas Classroom Teachers Association), Austin Schneider, Michael (Texas Association of Broadcasters), Austin Smith, Tom "Smitty" (Public Citizen), Austin Walter, Paul (FOI Foundation of Texas), Dallas Woodford, Suzy (Common Cause), Austin SB 279 Registering, but not testifying: For: Smith, Tom "Smitty" (Public Citizen), Austin SB 439 For: Mullins, Renee Byrd (Self & Family), Wahiawa, HI Registering, but not testifying: For: Delong, Andrew D. (Self), Austin Garcia, Dianne Hardy (Lesbian/Gay Rights Lobby of Texas), Austin Gutierrez, Jesse (Mexican American Legal Defense Fund), San Antonio Hinojosa, Walter (Texas AFL-CIO), Austin Hood, Sonny (Self), Austin Jacobson, Joseph (ACLU of Texas), Austin Messina, Mary Anne (Self), Austin Mitchell, Beth (Advocacy, Inc.), Austin Zuniga, Manuel, Jr. (Self), Austin Against: Martino, Kelly (Self), Austin On: Buck, Renee (Self), Austin Conover, Bill (Texas Commission on Human Rights), Austin Smith, Tom "Smitty" (Public Citizen), Austin Written materials submitted: Against: Cheng, Albert (Free Market Committee), Dallas Ford, Richard (Free Market Committee), Dallas SB 613 Registering, but not testifying: For: Schneider, Michael (Texas Association of Broadcasters), Austin Smith, Tom "Smitty" (Public Citizen), Austin Woodford, Suzy (Common Cause), Austin On: Fawcett, Scott (Office of the Comptroller), Austin Koebele, Steve (Office of the Comptroller), Austin