crnt4sbml.SemiDiffusiveApproach¶
-
class
crnt4sbml.
SemiDiffusiveApproach
(cgraph, get_physiological_range)[source]¶ Class for constructing variables and methods needed for the semi-diffusive approach.
-
__init__
(cgraph, get_physiological_range)[source]¶ Initialization of the SemiDiffusiveApproach class.
Methods
__init__
(cgraph, get_physiological_range)Initialization of the SemiDiffusiveApproach class. generate_report
()Prints out helpful details constructed by crnt4sbml.SemiDiffusiveApproach.run_optimization()
andcrnt4sbml.SemiDiffusiveApproach.run_continuity_analysis()
.get_key_species
()Returns a list of string variables corresponding to the key species. get_non_key_species
()Returns a list of string variables corresponding to those species that are not key species. get_boundary_species
()Returns a list of string variables corresponding to those species that are defined as boundary species. get_decision_vector
()Returns a list of SymPy variables corresponding to the decision vector for the optimization problem. print_decision_vector
()Prints an easily readable form of the decision vector. get_mu_vector
()Returns a list of SymPy variables corresponding to the vector of fluxes, . get_s_to_matrix
()Returns SymPy matrix representing the matrix. get_y_r_matrix
()Returns SymPy matrix representing the matrix. get_symbolic_polynomial_fun
()Returns SymPy matrix representing the vector of polynomial functions, . get_lambda_polynomial_fun
()Returns a list of lambda functions for the vector of polynomial functions. get_symbolic_objective_fun
()Returns SymPy expression for the objective function of the optimization problem. get_lambda_objective_fun
()Returns a lambda function representation of the objective function of the optimization problem. get_optimization_bounds
()Returns a list of tuples defining the upper and lower bounds for the decision vector variables based on physiological ranges. get_my_rank
()Returns the rank assigned by mpi4py if it is initialized, otherwise None will be returned. get_comm
()Returns a mpi4py communicator if it has been initialized and None otherwise. run_optimization
([bounds, iterations, …])Function for running the optimization problem for the semi-diffusive approach. run_continuity_analysis
([species, …])Function for running the numerical continuation and bistability analysis portions of the semi-diffusive approach. run_greedy_continuity_analysis
([species, …])Function for running the greedy numerical continuation and bistability analysis portions of the semi-diffusive approach. -
generate_report
()[source]¶ Prints out helpful details constructed by
crnt4sbml.SemiDiffusiveApproach.run_optimization()
andcrnt4sbml.SemiDiffusiveApproach.run_continuity_analysis()
.Example
See Semi-diffusive Approach Example and Semi-diffusive Approach Walkthrough.
-
get_boundary_species
()[source]¶ Returns a list of string variables corresponding to those species that are defined as boundary species.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> print(approach.get_boundary_species()) ['s21']
-
get_decision_vector
()[source]¶ Returns a list of SymPy variables corresponding to the decision vector for the optimization problem.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> print(approach.get_decision_vector()) [v_2, v_3, v_4, v_5, v_6, v_7, v_9, v_11, v_13, v_15, v_17, v_18]
-
get_key_species
()[source]¶ Returns a list of string variables corresponding to the key species.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> print(approach.get_key_species()) ['s1', 's2', 's7']
-
get_lambda_objective_fun
()[source]¶ Returns a lambda function representation of the objective function of the optimization problem. Here the arguments of the lambda function are given by the values provided by
crnt4sbml.SemiDiffusiveApproach.get_mu_vector()
.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/sbml_file.xml") >>> approach = network.get_semi_diffusive_approach() >>> approach.get_lambda_objective_fun()
-
get_lambda_polynomial_fun
()[source]¶ Returns a list of lambda functions for the vector of polynomial functions. The index of the list corresponds to the row in the vector of polynomial functions. Here the arguments of the lambda function are given by the values provided by
crnt4sbml.SemiDiffusiveApproach.get_mu_vector()
.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/sbml_file.xml") >>> approach = network.get_semi_diffusive_approach() >>> approach.get_lambda_polynomial_fun()
-
get_mu_vector
()[source]¶ Returns a list of SymPy variables corresponding to the vector of fluxes, .
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> print(approach.get_mu_vector()) [v_1, v_2, v_3, v_4, v_5, v_6, v_7, v_8, v_9, v_11, v_13, v_15, v_16, v_17, v_18, v_19]
-
get_my_rank
()[source]¶ Returns the rank assigned by mpi4py if it is initialized, otherwise None will be returned.
-
get_non_key_species
()[source]¶ Returns a list of string variables corresponding to those species that are not key species.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> print(approach.get_non_key_species()) ['s3', 's6', 's8', 's11']
-
get_optimization_bounds
()[source]¶ Returns a list of tuples defining the upper and lower bounds for the decision vector variables based on physiological ranges.
Fig1Cii.xml
for the provided example.Examples
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> bounds = approach.get_optimization_bounds() >>> print(bounds) [(0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55), (0, 55)]
-
get_s_to_matrix
()[source]¶ Returns SymPy matrix representing the matrix. The columns of which correspond to the true and outflow reactions of the stoichiometric matrix.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> sympy.pprint(approach.get_s_to_matrix()) ⎡-1 1 0 0 -1 1 0 1 0 -1 0 0 0 0 0 0 ⎤ ⎢ ⎥ ⎢-1 1 0 0 0 0 1 0 0 0 -1 0 0 0 0 0 ⎥ ⎢ ⎥ ⎢1 -1 0 0 0 0 -1 0 0 0 0 0 0 -1 0 0 ⎥ ⎢ ⎥ ⎢0 0 -1 1 -1 1 1 0 2 0 0 0 -1 0 0 0 ⎥ ⎢ ⎥ ⎢0 0 -1 1 0 0 0 1 0 0 0 -1 0 0 0 0 ⎥ ⎢ ⎥ ⎢0 0 1 -1 0 0 0 -1 0 0 0 0 0 0 -1 0 ⎥ ⎢ ⎥ ⎣0 0 0 0 1 -1 0 0 -1 0 0 0 0 0 0 -1⎦
-
get_symbolic_objective_fun
()[source]¶ Returns SymPy expression for the objective function of the optimization problem. This is the determinant of squared.
Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/sbml_file.xml") >>> approach = network.get_semi_diffusive_approach() >>> approach.get_symbolic_objective_fun()
-
get_symbolic_polynomial_fun
()[source]¶ Returns SymPy matrix representing the vector of polynomial functions, .
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> sympy.pprint(approach.get_symbolic_polynomial_fun()) ⎡ v₁ + v₁₁ - v₂ + v₅ - v₆ - v₈ ⎤ ⎢ ⎥ ⎢ v₁ + v₁₃ - v₂ - v₇ ⎥ ⎢ ⎥ ⎢ -v₁ + v₁₇ + v₂ + v₇ ⎥ ⎢ ⎥ ⎢v₁₆ + v₃ - v₄ + v₅ - v₆ - v₇ - 2⋅v₉⎥ ⎢ ⎥ ⎢ v₁₅ + v₃ - v₄ - v₈ ⎥ ⎢ ⎥ ⎢ v₁₈ - v₃ + v₄ + v₈ ⎥ ⎢ ⎥ ⎣ v₁₉ - v₅ + v₆ + v₉ ⎦
-
get_y_r_matrix
()[source]¶ Returns SymPy matrix representing the matrix. The columns of which correspond to the true and outflow reactions of the molecularity matrix.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> import sympy >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> sympy.pprint(approach.get_y_r_matrix()) ⎡1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0⎤ ⎢ ⎥ ⎢1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0⎥ ⎢ ⎥ ⎢0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0⎥ ⎢ ⎥ ⎢0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0⎥ ⎢ ⎥ ⎢0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0⎥ ⎢ ⎥ ⎢0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0⎥ ⎢ ⎥ ⎣0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1⎦
-
print_decision_vector
()[source]¶ Prints an easily readable form of the decision vector. It first prints the decision vector and then the corresponding reaction labels.
Fig1Cii.xml
for the provided example.Example
>>> import crnt4sbml >>> network = crnt4sbml.CRNT("path/to/Fig1Cii.xml") >>> approach = network.get_semi_diffusive_approach() >>> approach.print_decision_vector() Decision vector for optimization: [v_2, v_3, v_4, v_5, v_6, v_7, v_9, v_11, v_13, v_15, v_17, v_18] Reaction labels for decision vector: ['re1r', 're3', 're3r', 're6', 're6r', 're2', 're8', 're17r', 're18r', 're19r', 're21', 're22']
-
run_continuity_analysis
(species=None, parameters=None, dir_path='./num_cont_graphs', print_lbls_flag=False, auto_parameters=None, plot_labels=None)[source]¶ Function for running the numerical continuation and bistability analysis portions of the semi-diffusive approach.
Parameters: - species (string) – A string stating the species that is the y-axis of the bifurcation diagram.
- parameters (list of numpy arrays) – A list of numpy arrays corresponding to the decision vectors that produce a small objective function value.
- dir_path (string) – A string stating the path where the bifurcation diagrams should be saved.
- print_lbls_flag (bool) – If True the routine will print the special points found by AUTO 2000 and False will not print any special points.
- auto_parameters (dict) – Dictionary defining the parameters for the AUTO 2000 run. Please note that one should not set
‘SBML’ or ‘ScanDirection’ in these parameters as these are automatically assigned. It is absolutely
necessary to set PrincipalContinuationParameter in this dictionary. For more information on these
parameters refer to
AUTO parameters
. ‘NMX’ will default to 10000 and ‘ITMX’ to 100. - plot_labels (list of strings) – A list of strings defining the labels for the x-axis, y-axis, and title. Where the first element is the label for x-axis, second is the y-axis label, and the last element is the title label. If you would like to use the default settings for some of the labels, simply provide None for that element.
Returns: - multistable_param_ind (list of integers) – A list of those indices in ‘parameters’ that produce multistable plots.
- plot_specifications (list of lists) – A list whose elements correspond to the plot specifications of each element in multistable_param_ind. Each element is a list where the first element specifies the range used for the x-axis, the second element is the range for the y-axis, and the last element provides the x-y values and special point label for each special point in the plot.
Example
See Semi-diffusive Approach Example and Semi-diffusive Approach Walkthrough.
-
run_greedy_continuity_analysis
(species=None, parameters=None, dir_path='./num_cont_graphs', print_lbls_flag=False, auto_parameters=None, plot_labels=None)[source]¶ Function for running the greedy numerical continuation and bistability analysis portions of the semi-diffusive approach. This routine uses the initial value of the principal continuation parameter to construct AUTO parameters and then tests varying fixed step sizes for the continuation problem. Note that this routine may produce jagged or missing sections in the plots provided. To produce better plots one should use the information provided by this routine to run
crnt4sbml.SemiDiffusiveApproach.run_continuity_analysis()
.Parameters: - species (string) – A string stating the species that is the y-axis of the bifurcation diagram.
- parameters (list of numpy arrays) – A list of numpy arrays corresponding to the decision vectors that produce a small objective function value.
- dir_path (string) – A string stating the path where the bifurcation diagrams should be saved.
- print_lbls_flag (bool) – If True the routine will print the special points found by AUTO 2000 and False will not print any special points.
- auto_parameters (dict) – Dictionary defining the parameters for the AUTO 2000 run. Please note that only the
PrincipalContinuationParameter in this dictionary should be defined, no other AUTO parameters should
be set. For more information on these parameters refer to
AUTO parameters
. - plot_labels (list of strings) – A list of strings defining the labels for the x-axis, y-axis, and title. Where the first element is the label for x-axis, second is the y-axis label, and the last element is the title label. If you would like to use the default settings for some of the labels, simply provide None for that element.
Returns: - multistable_param_ind (list of integers) – A list of those indices in ‘parameters’ that produce multistable plots.
- plot_specifications (list of lists) – A list whose elements correspond to the plot specifications of each element in multistable_param_ind. Each element is a list where the first element specifies the range used for the x-axis, the second element is the range for the y-axis, and the last element provides the x-y values and special point label for each special point in the plot.
Example
-
run_optimization
(bounds=None, iterations=10, sys_min_val=2.220446049250313e-16, seed=0, print_flag=False, numpy_dtype=<class 'numpy.float64'>, confidence_level_flag=False, change_in_rel_error=0.01, parallel_flag=False)[source]¶ Function for running the optimization problem for the semi-diffusive approach. Note that there are no bounds enforced on species’ concentrations as they are automatically restricted to be greater than zero by the theory.
Parameters: - bounds (list of tuples) – A list defining the lower and upper bounds for each variable in the decision vector. Here the reactions are allowed to be set to a single value.
- iterations (int) – The number of iterations to run the feasible point method.
- sys_min_val (float) – The value that should be considered zero for the optimization problem.
- seed (int) – Seed for the random number generator. None should be used if a random generation is desired.
- print_flag (bool) – Should be set to True if the user wants the objective function values found in the optimization problem and False otherwise.
- numpy_dtype – The numpy data type used within the optimization routine. All variables in the optimization routine will be converted to this data type.
- confidence_level_flag (bool) – If True a confidence level for the objective function will be given.
- change_in_rel_error (float) – The maximum relative error that should be allowed to consider in the neighborhood of .
- parallel_flag (bool) – If set to True a parallel version of the optimization routine is ran. If False, a serial version of the optimization routine is ran. See Parallel General Approach.
Returns: - params_for_global_min (list of numpy arrays) – A list of numpy arrays that correspond to the decision vectors of the problem.
- obj_fun_val_for_params (list of floats) – A list of objective function values produced by the corresponding decision vectors in params_for_global_min.
Examples
See Semi-diffusive Approach Example and Semi-diffusive Approach Walkthrough.
-


{% extends "ab_tool/base_lti.html" %} {% load static %} {% block stylesheet %} {{ block.super }} {% endblock %} {% block content %}
Manage the tracks in this experiment and the probability that a learner will be sorted into each track. Tracks may have explicit weightings (weighted random) or be weighted equally (uniform random).
Note: As these are probabilities, the actual proportion of students in each track may differ from the probabilities specified.


| $$$ | Time Machine Power | Gold Mining Infrastructure | Quarry Depth |
|---|---|---|---|
| {{gameState.money | number : '1.2-2'}} | {{gameState.timeTravel.intervalLevel}} | {{gameState.mineLevel}} | {{gameState.quarryLevel}} |
| {{gameState.income | number : '1.2-2'}} $/s |
| Entropy / Sigils | Time Crystal Layers | Butterfly Resonator Strength | Paradox Engine Throttle |
|---|---|---|---|
| {{gameState.timeTravel.entropy }} / {{gameState.timeTravel.getFreeLevels()}} | {{gameState.timeTravel.timeCrystalLevel}} | {{gameState.timeTravel.butterflyResonatorLevel}} | {{gameState.timeTravel.paradoxEngineLevel}} |
| Next increase = {{(gameState.timeTravel.intervalLevel + 1 )%3 + 1}} |
| Day {{gameState.adventure.days}} |
|---|
| {{msg}} |


Sorry, this example cannot be run because your browser does not support the <canvas> element
Cheatcode:
Submit
Level:
Check "Hard"
Uncheck "Hard"
Day/Night:
Submit
Lives:
Submit
Name:
Submit
Enemies colors:
Submit
START
SETTINGS
SUBMIT
HIGHSCORE
BACK


The first thing to point out is that Services are OPTIONAL - you do not need to set up any services unless you feel they would help or be useful to you.
What is a Service?
A Service is an arbitrarily define entity that can be associated with resource. Services sit below resources in the structure hierarchy.
Why use Services?
If your resource can be booked to perform multiple tasks. Creating services for a resource allows your user to choose the resource AND what service is to be performed. An example would be a staff member in a Spa, she can be booked to do various services, massage, manicure, etc.
Things to know about Services.
You can create as many services as you wish for a resource. A service is specific to a resource but you can copy services to other resources. For example, if your Spa has three staff and all do massage, you can copy the 'massage' service to the other staff members (resources).
Services and services can be used together, they have no real relation to one another.
The service selected by the user will appear in the booking detail.
Hierarchy
Resource Service -> Resource -> Resource Service
Publish: Sets one or more services to Unpublished. Only Published services will appear in the drop down list on the Appointment Booking screen.
Unpublish: Sets one or more services to Unpublished so they will not show in the drop down list on the Appointment Booking screen.
Copy: Select one or more services to copy to another resource.
Remove: Deletes one or more services.
Edit: Opens the Service Edit screen to allow you to view and modify the Service.
New: Opens the New Service screen to allow you to add a new Service.
Help: Opens this help screen.
Resource: select the resource who's services you wish to see.
ID: Unique ID for the Service.
Name: Service name.
Description: Service description, for internal use, does not appear on user screen anywhere.
Resource: the resource this service is for.
Display Order: Order the services will appear in the Service drop down list.
Published: Indicates the published state.
Click on the Name link to open the edit screen.


# How to generate certificate
```bash
openssl genrsa -out key.pem
openssl req -new -key key.pem -out csr.pem
openssl x509 -req -days 9999 -in csr.pem -signkey key.pem -out cert.pem
rm csr.pem
```
You will get a cert.pem (the certificate) and key.pem (the private key). This is all you need for a SSL connection.
**In the second command, when prompted for “Common Name (eg, YOUR name) []:”, do not give your name.** It is actually the domain name field, so enter your domain name. Not giving your domain name will result in “domain mismatch” errors.
This certificate will trigger warnings in your browser, since it is self-signed.


search in the most popular groups search in the other groups
Copyright © 2006-2017 binsearch - disclaimer


PoGo Series: Statistical Analysis of Data
31 Jul 2016Table of Contents:
- Introduction to the Series
- The Twitter API
- The Tweepy Library
- Naive Bayes Classifiers
- Training a Sentiment Analyzer
- Statistical Analysis of the Data
- Visualizing the Data with Choropleth Maps
Welcome to the second-to-last post in our Pokemon Go analysis series. Last time, we trained a sentiment analyzer that will allow us to remove negative tweets from our collection. In this post, we’ll use this analyzer to clean up our data and determine the dominance of each Pokemon Go team in each state. Once we’ve cleaned the data, we’ll count the number of tweets referencing each Pokemon Go team in every state. In the end, we’ll use the fraction of tweets referencing each team as a measure of the dominance of the teams in each state. Along the way, we’ll discuss how to properly account for statistical and systematic errors in our measurement.
We’ll cover the following topics:
- Cleaning the data
- Finding the location of each tweet
- Measuring team dominance in each state
- Statistical analysis of the data
Cleaning the Data
Before we begin, we’ll need to load the our collection of tweets into Pandas. We already did this in our last post, and saved to the result to a csv file. We can load that csv file with the following code:
#This is needed to load our csv
import pandas as pd
#We'll also use numpy to do some math later
import numpy as np
#Load the full collection of PoGo tweets
PoGo_tweets = pd.read_csv('PoGo_Sentiment_AllTweets.csv')
PoGo_tweets.drop('Unnamed: 0', axis=1, inplace=True)
PoGo_tweets.head(n=5)
| screenName | userId | text | location | multi-team | |
|---|---|---|---|---|---|
| 0 | desmond_ayala | 2.953472e+09 | Which pokemon go team did y'all chose? #valor | Caldwell, ID | False |
| 1 | aphrospice | 1.629086e+07 | #Magikarp practicing his struggle skills in th... | Brooklyn, NY | False |
| 2 | ABellgowan | 1.681036e+09 | Pokemon Go is taking over my life #TeamInstinct | Bixby, OK | False |
| 3 | JangoSnow | 1.057434e+07 | Go Team Instinct! I like underdogs. :) https:... | Los Angeles, CA | False |
| 4 | EmberLighta2 | 7.513920e+17 | #TeamMystic has total control of Niagara Falls!! | Niagara Falls, NY | False |
Recall that the csv file has already had all tweets that reference multiple teams removed, since we decided it would be too hard to identify which team the Twitter user was on from such tweets.
We’d like to remove any negatively-toned tweets from our collection so that we can assume any remaining tweets about the team indicates that the Twitter user is a member of that team. We can do so by applying the sentiment analyzer that we developed in our last post. First, we use pickle
to load the sentiment analyzer and feature list that we created.
#load the classifier
import pickle
f = open('PoGo_tweet_classifier.pickle', 'rb')
classifier = pickle.load(f)
f.close()
#load the list of features used in the classifier
with open('PoGo_classifier_feats.pickle', 'rb') as f:
word_features = pickle.load(f)
We’ll also need to use the filter_tweets
and extract_features
functions that we wrote for our sentiment analyzer. In this case, I’ve slightly modified the filter_tweets
function — it now produces a list of filtered unigrams and bigrams for each tweet instead of a tuple, since we have no manually labeled sentiment to pair with such a list.
#Load the required modules
import nltk
from nltk.metrics import BigramAssocMeasures
from nltk.collocations import BigramCollocationFinder
import itertools
import string
#Set of punctuation to exclude from unigrams and bigrams
exclude = set(string.punctuation)
#List of team-identifying words to exclude from unigrams and bigrams
excluded_words = ['teammystic','mystic','teamblue','blue',\
'teaminstinct','instinct','teamyellow','yellow',\
'teamvalor','valor','teamred','red']
#Function that provides a list of filtered unigrams and bigrams from each tweet
def filter_tweets(tweet_text):
words_filtered=[]
#For each word in the tweet, filter on our feature requirements.
for word in tweet_text.split():
#Remove punctuation
word = ''.join(ch for ch in word if ch not in exclude)
#Remove one letter words
if len(word) >= 1:
#treat URLs the same
if word[:4] == 'http':
word='http'
#remove hashtags
if word[0] == '#':
word=word[1:]
#remove team identifiers
if (word.lower() not in excluded_words):
#require lower case
words_filtered.append(word.lower())
#If the word list contains only duplicates of one word, it causes problems for bigram finder
#In this case, don't bother trying to find bigrams, just find the unigram since there are no bigrams anyway
if len(set(words_filtered)) == 1:
tweet_feats = words_filtered[0]
else:
#Identify top 200 bigams in the filtered word list using chi_sq measure of importance
bigram_finder = BigramCollocationFinder.from_words(words_filtered)
bigrams = bigram_finder.nbest(BigramAssocMeasures.chi_sq, 200)
tweet_feats = [ngram for ngram in itertools.chain(words_filtered, bigrams)]
return tweet_feats
#Feature extractor - determines which word features are in each tweet
def extract_features(filtered_tweet):
#list of unigrams and bigrams in the tweet
filtered_tweet_words = set(filtered_tweet)
#Define a features dictionary
features = {}
#Loop of all word features
for word in word_features:
#Set 'contains(word_feature)' as a key in the dictionary
#Set the value for that key to True or False
features['contains(%s)' % str(word)] = (word in filtered_tweet_words)
#Return the final features dictionary for that tweet
return features
To classify the sentiment of each tweets in our collection, we first apply the filter_tweets
function to extract the filtered unigrams and bigrams from the tweet. We use that list of unigrams and bigrams to evaluate the contains(word_feature)
statements which we used as features for our sentiment analyzer. Finally, we pass the feature values to our sentiment analyzer and place the result in a new “sentiment” column in our data frame.
#Classifying tweets
def ClassifyTweets(dFrame):
for row in range(len(dFrame)):
#Get filtered unigrams and bigrams
filtered_text = filter_tweets(dFrame.ix[row,'text'])
#Evaluate the contains(word) statements
tweet_feats = extract_features(filtered_text)
#Add result to new sentimenet column in the dataframe
dFrame.ix[row,'sentiment']=classifier.classify(tweet_feats)
ClassifyTweets(PoGo_tweets)
PoGo_tweets.head(n=5)
| screenName | userId | text | location | multi-team | sentiment | |
|---|---|---|---|---|---|---|
| 0 | desmond_ayala | 2.953472e+09 | Which pokemon go team did y'all chose? #valor | Caldwell, ID | False | positive |
| 1 | aphrospice | 1.629086e+07 | #Magikarp practicing his struggle skills in th... | Brooklyn, NY | False | positive |
| 2 | ABellgowan | 1.681036e+09 | Pokemon Go is taking over my life #TeamInstinct | Bixby, OK | False | positive |
| 3 | JangoSnow | 1.057434e+07 | Go Team Instinct! I like underdogs. :) https:... | Los Angeles, CA | False | positive |
| 4 | EmberLighta2 | 7.513920e+17 | #TeamMystic has total control of Niagara Falls!! | Niagara Falls, NY | False | positive |
If a state in the US has a lot of Twitter users we’ll have plenty of tweets for our team dominance measurement. In this case, removing tweets that our sentiment analyzer has identified as negative will reduce the total error on our measurement. However, if a particular state has very few tweets about each Pokemon Go team, our sentiment analyzer will reduce our already small sample size. In turn, this will increase the total error on our team dominance measurement. I’ll explain why this is the case in the upcoming statistical analysis sections, but for now it is sufficient to know that we will not always want to remove the negative tweets from our data set. Therefore, we must create two branches of our data frame — one in which we do remove negative tweets, and one in which we do not.
#Creating a new data frame where we do not apply sentiment analysis
PoGo_tweets_nosenti = PoGo_tweets
#Applying the positive sentiment restriction on the original dataframe
PoGo_tweets = PoGo_tweets[PoGo_tweets['sentiment'] == 'positive']
PoGo_tweets = PoGo_tweets.reset_index(drop=True)
Next, we’ll want to require that each tweet is coming from a unique user. If there happens to be a Twitter addict who Tweets about their Pokemon Go team every hour, we don’t want to count them as a member of that team multiple times. We can ensure this doesn’t happen by indicating the first tweet from each user in a new column of our data set.
def FindUniqueUsers(dFrame):
unique_users=[]
#Loop through the tweets in our dataframe
for row in range(len(dFrame)):
#If we have already seen this Twitter user, flag the tweet as a repeat
if dFrame.ix[row,'screenName'] in unique_users:
dFrame.ix[row,'repeatUser'] = True
#Otherwise, add the user to a list so we can identify if they tweet again
else:
dFrame.ix[row,'repeatUser'] = False
unique_users.append(dFrame.ix[row,'screenName'])
#Apply the unique user function to both of our data frames
FindUniqueUsers(PoGo_tweets_nosenti)
FindUniqueUsers(PoGo_tweets)
#Remove all but the first tweet from each user in our data frames
PoGo_tweets = PoGo_tweets[PoGo_tweets['repeatUser'] == False]
PoGo_tweets_nosenti = PoGo_tweets_nosenti[PoGo_tweets_nosenti['repeatUser'] == False]
#Reindex the data frames
PoGo_tweets = PoGo_tweets.reset_index(drop=True)
PoGo_tweets_nosenti = PoGo_tweets_nosenti.reset_index(drop=True)
Finding the location of each tweet
Now that we have removed multi-team tweets, negatively-toned tweets, and repeated tweets from the same user from our collection, we are ready to count the number of tweets about each Pokemon Go team in each state. First, we need to determine which state each tweet came from. The location column of our data frame almost does this for us, but as you can see below, some tweets have location information in the format State, Country
, while others have location information in the format City, State Abbreviation
.
PoGo_tweets.tail(n=3)
| screenName | userId | text | location | multi-team | sentiment | repeatUser | |
|---|---|---|---|---|---|---|---|
| 9434 | TheJakeTyler | 42497434.0 | #TeamMystic let's get it!!!! | Virginia, USA | False | positive | False |
| 9435 | LivAboveAverage | 333182239.0 | @AnthonyChott GO TEAM MYSTIC!!! #TeamMystic | Chesterfield, MO | False | positive | False |
| 9436 | TylerDurden919 | 334394891.0 | 10:45 Saturday morning, everyone's at @Bojangl... | Durham, NC | False | positive | False |
We’d like to extract the state information, regardless of which location format is being used. To do so, we first define a dictionary of state abbreviations and the corresponding state name. We also will want to create stand-alone lists of state abbreviations and state names from this dictionary.
#Dictionary of state abbreviations and names
states = {
'AK': 'Alaska',
'AL': 'Alabama',
'AR': 'Arkansas',
'AZ': 'Arizona',
'CA': 'California',
'CO': 'Colorado',
'CT': 'Connecticut',
'DC': 'District of Columbia',
'DE': 'Delaware',
'FL': 'Florida',
'GA': 'Georgia',
'HI': 'Hawaii',
'IA': 'Iowa',
'ID': 'Idaho',
'IL': 'Illinois',
'IN': 'Indiana',
'KS': 'Kansas',
'KY': 'Kentucky',
'LA': 'Louisiana',
'MA': 'Massachusetts',
'MD': 'Maryland',
'ME': 'Maine',
'MI': 'Michigan',
'MN': 'Minnesota',
'MO': 'Missouri',
'MS': 'Mississippi',
'MT': 'Montana',
'NC': 'North Carolina',
'ND': 'North Dakota',
'NE': 'Nebraska',
'NH': 'New Hampshire',
'NJ': 'New Jersey',
'NM': 'New Mexico',
'NV': 'Nevada',
'NY': 'New York',
'OH': 'Ohio',
'OK': 'Oklahoma',
'OR': 'Oregon',
'PA': 'Pennsylvania',
'PR': 'Puerto Rico',
'RI': 'Rhode Island',
'SC': 'South Carolina',
'SD': 'South Dakota',
'TN': 'Tennessee',
'TX': 'Texas',
'UT': 'Utah',
'VA': 'Virginia',
'VT': 'Vermont',
'WA': 'Washington',
'WI': 'Wisconsin',
'WV': 'West Virginia',
'WY': 'Wyoming'
}
#Create a list of state abbreviations
state_abbrevs=[state for state in states]
#Create a list of state names
state_names=[states[state] for state in states]
For each tweet in our data frame, we can scan the location information to look for the state abbreviations or state names from our dictionary. Some location information, such as “New York, NY” or “Alabama, New York” (yes, Alabama is a real city in New York) will contain multiple state names or abbreviations. In this case, we’ll want to identify the second name or abbreviation that appears in the location information. The one exception to this rule is users from “District of Columbia, Washington,” where we’ll want to use the first “state” name (District of Columbia) that appears. Once we’ve extracted the state from the location information, we’ll add it to a new column in our data frame.
#Function to determine the state that each tweet originated in
def GetTweetState(dFrame):
#Get a list of all the tweet's locations from our dataframe
loc_list=list(dFrame['location'])
state_list=[]
#Loop over the location info for each tweet
for info in loc_list:
#See if a state abbreviation or name is in the location information
new_state = [state for state in state_names if state in info] + \
[states[state] for state in states if state in info]
#Handle cases like "New York, NY" and "Alabama, New York"
if (len(new_state) > 1) and new_state[0] != 'District of Columbia':
new_state = new_state[1].split('junk')
#Handle "District of Columbia, Washington"
elif len(new_state) > 1 and new_state[0] == 'District of Columbia':
new_state = new_state[0].split('junk')
#Handle cases where no state is mentioned
if not new_state:
new_state = ['None']
#After we determine the state, add it to a list
state_list+=new_state
#Once we have the entire list of states, add it to the dataframe
dFrame['state']=state_list
GetTweetState(PoGo_tweets)
GetTweetState(PoGo_tweets_nosenti)
PoGo_tweets.tail(n=3)
| screenName | userId | text | location | multi-team | sentiment | repeatUser | state | |
|---|---|---|---|---|---|---|---|---|
| 9434 | TheJakeTyler | 42497434.0 | #TeamMystic let's get it!!!! | Virginia, USA | False | positive | False | Virginia |
| 9435 | LivAboveAverage | 333182239.0 | @AnthonyChott GO TEAM MYSTIC!!! #TeamMystic | Chesterfield, MO | False | positive | False | Missouri |
| 9436 | TylerDurden919 | 334394891.0 | 10:45 Saturday morning, everyone's at @Bojangl... | Durham, NC | False | positive | False | North Carolina |
Measuring team dominance in each state
Now that we’ve identified the state each tweet is from, we can count how many tweets about each Pokemon Go team we collected in each state. First, we initialize a new data frame with a row for each state.
#stateInfo and stateInfo_ns
def MakeStateInfo(dFrame):
dFrame['State']=states.values()
dFrame['Num Tweets'] = 0
dFrame['Num Red'] = 0
dFrame['Num Blue'] = 0
dFrame['Num Yellow'] = 0
stateInfo = pd.DataFrame()
stateInfo_nosenti = pd.DataFrame()
MakeStateInfo(stateInfo)
MakeStateInfo(stateInfo_nosenti)
stateInfo.head(n=5)
| State | Num Tweets | Num Red | Num Blue | Num Yellow | |
|---|---|---|---|---|---|
| 0 | Pennsylvania | 0 | 0 | 0 | 0 |
| 1 | Delaware | 0 | 0 | 0 | 0 |
| 2 | Nebraska | 0 | 0 | 0 | 0 |
| 3 | Michigan | 0 | 0 | 0 | 0 |
| 4 | Idaho | 0 | 0 | 0 | 0 |
The stateInfo
dataframe is indexed with numbers by default. Since each row represents a state, it would be more convenient to use the state names as the index. We can tell Pandas to do this using the set_index
method.
stateInfo = stateInfo.set_index(['State'])
stateInfo_nosenti = stateInfo_nosenti.set_index(['State'])
stateInfo.head(n=5)
| Num Tweets | Num Red | Num Blue | Num Yellow | |
|---|---|---|---|---|
| State | ||||
| Pennsylvania | 0 | 0 | 0 | 0 |
| Delaware | 0 | 0 | 0 | 0 |
| Nebraska | 0 | 0 | 0 | 0 |
| Michigan | 0 | 0 | 0 | 0 |
| Idaho | 0 | 0 | 0 | 0 |
Now, we simply loop over each tweet in our collection. For each tweet, we search for team-identifying phrases. Once we’ve identified the team the tweet is referring to, we increase the count for that team in the corresponding state by one.
#Lists of team identifying phrases
yellow_text = ['#teaminstinct','#teamyellow','#instinct','team instinct']
blue_text = ['#teammystic','#teamblue','#mystic','team mystic']
red_text = ['#teamvalor','#teamred','#valor','team valor']
#Function to populate the stateInfo data frame
def PopStateInfo(state_dFrame,tweet_dFrame):
#Loop over the tweet collection
for idx in range(len(tweet_dFrame)):
#If we have state information for the tweet
if tweet_dFrame.ix[idx,'state'] != 'None':
#Search for the team identifying phrases and
#Add one count to that team in state where the tweet came from
if any(hashtag in tweet_dFrame.ix[idx,'text'].lower() for hashtag in yellow_text):
state_dFrame.ix[tweet_dFrame.ix[idx,'state'],'Num Yellow'] += 1
elif any(hashtag in tweet_dFrame.ix[idx,'text'].lower() for hashtag in blue_text):
state_dFrame.ix[tweet_dFrame.ix[idx,'state'],'Num Blue'] += 1
elif any(hashtag in tweet_dFrame.ix[idx,'text'].lower() for hashtag in red_text):
state_dFrame.ix[tweet_dFrame.ix[idx,'state'],'Num Red'] += 1
#After we've process all tweets
#Count all the tweets in each state by adding red + blue + yellow
state_dFrame['Num Tweets'] = state_dFrame.sum(axis=1)
PopStateInfo(stateInfo,PoGo_tweets)
PopStateInfo(stateInfo_nosenti,PoGo_tweets_nosenti)
stateInfo.head(n=5)
| Num Tweets | Num Red | Num Blue | Num Yellow | |
|---|---|---|---|---|
| State | ||||
| Pennsylvania | 275 | 111 | 97 | 67 |
| Delaware | 27 | 13 | 7 | 7 |
| Nebraska | 64 | 26 | 23 | 15 |
| Michigan | 324 | 123 | 115 | 86 |
| Idaho | 27 | 11 | 9 | 7 |
Our metric for the dominance of each team in a state will simply be the fraction of tweets from that state that reference a particular team. We can easily add this fraction to our data frame
#Function to populate the stateInfo data frame
def CalcDominance(dFrame):
dFrame['R Frac']=dFrame['Num Red']/dFrame['Num Tweets']
dFrame['Y Frac']=dFrame['Num Yellow']/dFrame['Num Tweets']
dFrame['B Frac']=dFrame['Num Blue']/dFrame['Num Tweets']
CalcDominance(stateInfo)
CalcDominance(stateInfo_nosenti)
stateInfo.head(n=5)
| Num Tweets | Num Red | Num Blue | Num Yellow | R Frac | Y Frac | B Frac | |
|---|---|---|---|---|---|---|---|
| State | |||||||
| Pennsylvania | 275 | 111 | 97 | 67 | 0.403636 | 0.243636 | 0.352727 |
| Delaware | 27 | 13 | 7 | 7 | 0.481481 | 0.259259 | 0.259259 |
| Nebraska | 64 | 26 | 23 | 15 | 0.406250 | 0.234375 | 0.359375 |
| Michigan | 324 | 123 | 115 | 86 | 0.379630 | 0.265432 | 0.354938 |
| Idaho | 27 | 11 | 9 | 7 | 0.407407 | 0.259259 | 0.333333 |
Statistical Analysis of the data
In this section, I’ll discuss how to properly account for statistical and systematic errors in each of our tweet counts, and how to propagate those errors to our team dominance metric. Along the way, I’ll also determine how many tweets are needed for our sentiment analyzer to improve the total error of our measurement. I’ll provide hyperlinks that will detail the mathematical concepts we’ll be using, but without a strong understanding of statistics and calculus this section may be hard to follow.
Counting statistics
In statistics, a Poisson distribution is used to describe the number of times an event occurs within a time interval. This representation is appropriate if:
- The number of times the event occurs () in an interval is discrete (0,1,2,…)
- The event occurs independent of other events
- The rate at which events occur is approximately constant.
- The probability of events occurring at the same time is negligible.
- The probability of an event occurring in an interval is proportional to the length of the interval.
The number of Pokemon Go tweets within each state meets all of the above conditions, so we can use the Poisson distribution to determine the statistical error on our measurement. The standard deviation of a Poisson distribution is given by the square root of the number of events observed. (A derivation of this can be found in the previous link.) Therefore, the statistical error of the count of tweets () is simply:
From our last post, we know that 8.92% of our tweet collection is contaminated with negative tweets when we do not apply our sentiment analyzer. Once we apply our sentiment analyzer to remove negative tweets, this contamination falls to 2.4%. We can treat the contamination of negative tweets as a systematic uncertainty on the number of tweets we count in each state:
Since the statistical and systematic errors on our tweet counts are uncorrelated, we can add them in quadrature to find the total error on our tweet counts:
When is our sentiment analysis useful?
Now that we know how to calculate the total error on our tweet counts, we can determine how many tweets we need for our sentiment analyzer to be useful. Applying our sentiment analyzer will lower the total number of tweets in our collection. In turn, this will lower both the absolute statistical and systematic error of our measurement. It’s important to realize that even though the absolute error on our tweet counts decreases as the total number of tweets decreases, the relative size of the error when compared to the number of tweets can still increase. Instead, we must evaluate when our sentiment analyzer will lower the relative error of our measurement. That is, we need to determine when:
When we use our sentiment analyzer to reduce the number of negative tweets in our collection, any misclassified positive tweets will also be removed, and any misclassified negative tweets will remain. Therefore, the number of remaining tweets after applying our sentiment analyzer is equal to:
Where I’ve used the fact that the negative recall of our classifier was , and the positive recall of our classifier was .
Plugging Equation 5 into Equation 4 and simplifying yields the result that our classifier will improve the relative error on our measurement when
Propagating errors to our team dominance metric
To make our team dominance map, we want to determine the fraction of tweets from each state that reference a particular team. With the number of tweets about a team in a particular state given by , and the number of total tweets in that state given by , the dominance of a particular team in a state is given by:
To determine the error on the team dominance measurement, we need to propagate the individual errors from the numerator and denominator. The general equation for propagating the errors of numerous variables, , to some function of those variables is given by:
where is the covariance of the variables and . In the case of uncorrelated variables, , so the last term in Equation 7 vanishes.
As it stands, the variables in our team dominance measurement, and , are correlated, since is equal to the sum of the tweet counts for each individual team. To simplify our analysis, we can rewrite the team dominance equation in terms of the number of tweets referencing that team () and the number of tweets referencing a different team ():
Equation 8 has no correlated variables, so we don’t need to worry about measuring the covariance of any terms when propagating error measurements. Therefore, with the team dominance denoted as ,
We can use the product rule to calculate the partial derivative of with respect to :
Similarly, we can use the chain rule to calculate the partial derivative of with respect to :
Statistical Analysis Code
Below, I’ve included the code that implements our statistical analysis.
def PopErrInfo(dFrame,sys_err):
#Systematic errors from negative tweet contamination
y_sys_err = sys_err*dFrame['Num Yellow']
r_sys_err = sys_err*dFrame['Num Red']
b_sys_err = sys_err*dFrame['Num Blue']
#Statistical errors from Poisson counting
y_stat_err = dFrame['Num Yellow'] ** (1/2)
r_stat_err = dFrame['Num Red'] ** (1/2)
b_stat_err = dFrame['Num Blue'] ** (1/2)
#Total errors
y_tot_err = (y_stat_err ** 2 + y_sys_err **2) ** (1/2)
r_tot_err = (r_stat_err ** 2 + r_sys_err **2) ** (1/2)
b_tot_err = (b_stat_err ** 2 + b_sys_err **2) ** (1/2)
#Propagation of total errors to the team dominance metric
dFrame['R Frac Err']= ( (r_tot_err ** 2)*(dFrame['Num Blue'] + dFrame['Num Yellow'])**2 + \
dFrame['Num Red']**2 * (b_tot_err**2 + y_tot_err**2) ) ** (1/2) / \
(dFrame['Num Yellow'] + dFrame['Num Blue'] + dFrame['Num Red'])**2
dFrame['Y Frac Err']= ( (y_tot_err ** 2)*(dFrame['Num Blue'] + dFrame['Num Red'])**2 + \
dFrame['Num Yellow']**2 * (b_tot_err**2 + r_tot_err**2) ) ** (1/2) / \
(dFrame['Num Yellow'] + dFrame['Num Blue'] + dFrame['Num Red'])**2
dFrame['B Frac Err']= ( (b_tot_err ** 2)*(dFrame['Num Yellow'] + dFrame['Num Red'])**2 + \
dFrame['Num Blue']**2 * (y_tot_err**2 + r_tot_err**2) ) ** (1/2) / \
(dFrame['Num Yellow'] + dFrame['Num Blue'] + dFrame['Num Red'])**2
sys_err_nosenti= 0.0892
sys_err_senti = 0.0240
PopErrInfo(stateInfo,sys_err_senti)
PopErrInfo(stateInfo_nosenti,sys_err_nosenti)
stateInfo.head(n=5)
| Num Tweets | Num Red | Num Blue | Num Yellow | R Frac | Y Frac | B Frac | R Frac Err | Y Frac Err | B Frac Err | |
|---|---|---|---|---|---|---|---|---|---|---|
| State | ||||||||||
| Pennsylvania | 275 | 111 | 97 | 67 | 0.403636 | 0.243636 | 0.352727 | 0.030429 | 0.026448 | 0.029600 |
| Delaware | 27 | 13 | 7 | 7 | 0.481481 | 0.259259 | 0.259259 | 0.096439 | 0.084531 | 0.084531 |
| Nebraska | 64 | 26 | 23 | 15 | 0.406250 | 0.234375 | 0.359375 | 0.061806 | 0.053213 | 0.060367 |
| Michigan | 324 | 123 | 115 | 86 | 0.379630 | 0.265432 | 0.354938 | 0.027841 | 0.025192 | 0.027430 |
| Idaho | 27 | 11 | 9 | 7 | 0.407407 | 0.259259 | 0.333333 | 0.094828 | 0.084526 | 0.090961 |
Note that in a given state the number of tweets from one team may exceed 50 counts, while the number of tweets from a different team may not. In this case, the sentiment analyzer would improve the error on our measurement for one team, but not for the other. To resolve this ambiguity, I calculated the average relative error for each of our team dominance metrics. I then added a new column to our data frames that indicated if the sentiment analysis improved the average relative error for each state.
#relative error = frac_err/frac
def AverageRelErr(dFrame):
row_errs = []
for row in range(len(dFrame)):
rel_errs = []
if dFrame.ix[row,'Y Frac'] != 0:
rel_errs.append(dFrame.ix[row,'Y Frac Err']/dFrame.ix[row,'Y Frac'])
if dFrame.ix[row,'R Frac'] != 0:
rel_errs.append(dFrame.ix[row,'R Frac Err']/dFrame.ix[row,'R Frac'])
if dFrame.ix[row,'B Frac'] != 0:
rel_errs.append(dFrame.ix[row,'B Frac Err']/dFrame.ix[row,'B Frac'])
row_errs.append(np.average(rel_errs))
return row_errs
stateInfo['Average Rel Err']=AverageRelErr(stateInfo)
stateInfo_nosenti['Average Rel Err']=AverageRelErr(stateInfo_nosenti)
stateInfo['Improved'] = stateInfo['Average Rel Err'] <= stateInfo_nosenti['Average Rel Err']
stateInfo.head(n=5)
| Num Tweets | Num Red | Num Blue | Num Yellow | R Frac | Y Frac | B Frac | R Frac Err | Y Frac Err | B Frac Err | Average Rel Err | Improved | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| State | ||||||||||||
| Pennsylvania | 275 | 111 | 97 | 67 | 0.403636 | 0.243636 | 0.352727 | 0.030429 | 0.026448 | 0.029600 | 0.089287 | True |
| Delaware | 27 | 13 | 7 | 7 | 0.481481 | 0.259259 | 0.259259 | 0.096439 | 0.084531 | 0.084531 | 0.284132 | False |
| Nebraska | 64 | 26 | 23 | 15 | 0.406250 | 0.234375 | 0.359375 | 0.061806 | 0.053213 | 0.060367 | 0.182386 | False |
| Michigan | 324 | 123 | 115 | 86 | 0.379630 | 0.265432 | 0.354938 | 0.027841 | 0.025192 | 0.027430 | 0.081843 | True |
| Idaho | 27 | 11 | 9 | 7 | 0.407407 | 0.259259 | 0.333333 | 0.094828 | 0.084526 | 0.090961 | 0.277224 | False |
In the end, the sentiment analysis improved the average relative error of our team dominance measurement in the following states:
for state in stateInfo.index[stateInfo['Improved'] == True]:
print (state)
Pennsylvania
Michigan
Alabama
Puerto Rico
California
Tennessee
Illinois
Nevada
North Carolina
South Dakota
Washington
Minnesota
Kentucky
Florida
Ohio
Indiana
Massachusetts
New Jersey
Texas
Oklahoma
Georgia
Arizona
Virginia
New York
Maryland
Oregon
Now that we’ve finished our statistical analysis, let’s save the data frames so that we can visualize the results in our next and final blog post.
stateInfo.to_csv('stateInfo.csv')
stateInfo_nosenti.to_csv('stateInfo_nosenti.csv')


ui
Classes
AssemblyLineStatusView
VehicleMechanicView
CustomShopManagerView
GarageHolderView
LoginView
ManagerView
SchedulingView
StatisticsView
View


CVS Revision: $Revision 1.0$
this form has name = test1 method = get action = pass.html
this form has name = test2 method = get action = pass2.html
this form has id = test3 method = post action = pass3.html


public interface ScalableSurface
NativeSurface
implementation,
see setSurfaceScale(int[])
.| Modifier and Type | Field and Description |
|---|---|
static int |
AUTOMAX_PIXELSCALE
Setting surface-pixel-scale of 0, results in maximum platform dependent pixel-scale, i.e.
|
static int |
IDENTITY_PIXELSCALE
Setting surface-pixel-scale of 1, results in same pixel- and window-units.
|
| Modifier and Type | Method and Description |
|---|---|
int[] |
getCurrentSurfaceScale(int[] result)
Returns the current pixel scale of the associated
NativeSurface . |
int[] |
getNativeSurfaceScale(int[] result)
Returns the native pixel scale of the associated
NativeSurface
reflecting it's currently bound monitor surface resolution in pixels. |
int[] |
getRequestedSurfaceScale(int[] result)
Returns the requested pixel scale of the associated
NativeSurface . |
void |
setSurfaceScale(int[] pixelScale)
Request a pixel scale in x- and y-direction for the associated
NativeSurface . |
static final int IDENTITY_PIXELSCALE
static final int AUTOMAX_PIXELSCALE
void setSurfaceScale(int[] pixelScale)
NativeSurface
.
Default pixel scale request for both directions is AUTOMAX_PIXELSCALE
.
In case platform only supports uniform pixel scale, i.e. one scale for both directions,
either AUTOMAX_PIXELSCALE
or the maximum requested pixel scale component is used.
The requested pixel scale will be validated against platform limits before native scale-setup,
i.e. clipped to IDENTITY_PIXELSCALE
if not supported or clipped to the platform maximum.
It can be queried via getRequestedSurfaceScale(int[])
.
The actual realized pixel scale values of the NativeSurface
can be queried via getCurrentSurfaceScale(int[])
or
computed via surface.
convertToPixelUnits
(new int[] { 1, 1 })
pixelScale
- requested surface pixel scale int[2] values for x- and y-direction.int[] getRequestedSurfaceScale(int[] result)
NativeSurface
.result
- int[2] storage for the resultint[] getCurrentSurfaceScale(int[] result)
NativeSurface
.result
- int[2] storage for the resultint[] getNativeSurfaceScale(int[] result)
NativeSurface
reflecting it's currently bound monitor surface resolution in pixels.
The native pixel scale maybe used to determine the proper dpi
value of this NativeSurface
:
surfacePpMM = monitorPpMM * currentSurfaceScale / nativeSurfaceScale, with PpMM == pixel per millimeter
result
- int[2] storage for the resultCopyright 2010 JogAmp Community.


Akhtaruzzaman to stay on as Dhaka University VC for four years
by bdnews24.comProf Md Akhtaruzzaman, who was serving as the acting vice chancellor of Dhaka University, has been formally appointed to the post for four years.
The education ministry announced the decision on Sunday.
President Abdul Hamid made the appointment after the university's senate finalised the three nominees for the office on July 31.
Pro Vice-Chancellor (Administration) Prof Muhammad Samad and Teachers Association President Prof ASM Maksud Kamal were the other members of the panel.
Prof Akhtaruzzaman was appointed as the ‘interim’ vice-chancellor of Dhaka University in September 2017, having served as the pro vice-chancellor (administration) until then.


| Top |
GObject ╰── GInitiallyUnowned ╰── GtkWidget ╰── GtkContainer ╰── GtkBin ╰── GtkViewport
The GtkViewport widget acts as an adaptor class, implementing scrollability for child widgets that lack their own scrolling capabilities. Use GtkViewport to scroll child widgets such as GtkGrid, GtkBox, and so on.
If a widget has native scrolling abilities, such as GtkTextView,
GtkTreeView or GtkIconView, it can be added to a GtkScrolledWindow
with gtk_container_add()
. If a widget does not, you must first add the
widget to a GtkViewport, then add the viewport to the scrolled window.
gtk_container_add()
does this automatically if a child that does not
implement GtkScrollable is added to a GtkScrolledWindow, so you can
ignore the presence of the viewport.
The GtkViewport will start scrolling content only if allocated less than the child widget’s minimum size in a given orientation.
GtkWidget * gtk_viewport_new (GtkAdjustment *hadjustment
,GtkAdjustment *vadjustment
);
Creates a new GtkViewport with the given adjustments, or with default adjustments if none are given.
GtkAdjustment *
gtk_viewport_get_hadjustment (GtkViewport *viewport
);
gtk_viewport_get_hadjustment
has been deprecated since version 3.0 and should not be used in newly-written code.
Returns the horizontal adjustment of the viewport.
GtkAdjustment *
gtk_viewport_get_vadjustment (GtkViewport *viewport
);
gtk_viewport_get_vadjustment
has been deprecated since version 3.0 and should not be used in newly-written code.
Returns the vertical adjustment of the viewport.
void gtk_viewport_set_hadjustment (GtkViewport *viewport
,GtkAdjustment *adjustment
);
gtk_viewport_set_hadjustment
has been deprecated since version 3.0 and should not be used in newly-written code.
Sets the horizontal adjustment of the viewport.
void gtk_viewport_set_vadjustment (GtkViewport *viewport
,GtkAdjustment *adjustment
);
gtk_viewport_set_vadjustment
has been deprecated since version 3.0 and should not be used in newly-written code.
Sets the vertical adjustment of the viewport.
void gtk_viewport_set_shadow_type (GtkViewport *viewport
,GtkShadowType type
);
Sets the shadow type of the viewport.
GtkShadowType
gtk_viewport_get_shadow_type (GtkViewport *viewport
);
Gets the shadow type of the GtkViewport. See
gtk_viewport_set_shadow_type()
.
GdkWindow *
gtk_viewport_get_bin_window (GtkViewport *viewport
);
Gets the bin window of the GtkViewport.
Since: 2.20
GdkWindow *
gtk_viewport_get_view_window (GtkViewport *viewport
);
Gets the view window of the GtkViewport.
Since: 2.22
“shadow-type”
property“shadow-type” GtkShadowType
Determines how the shadowed box around the viewport is drawn.
Flags: Read / Write
Default value: GTK_SHADOW_IN


Accessibility Checklist
Scroll to top
Print checklist
Clear the checklist
All
Incomplete
Complete
{{ section.section }}
{{ check.title }}
Read more about {{ check.title }}
Link to task


--- layout: documentation title: UnformattedMessageListType ---
The UnformattedMessageListType type is a list of unformatted messages in the event log entry.
| Field Name | Type | Description |
|---|---|---|
| Unformatted_Message1..n | StringObjectPropertyType |
A single unformatted message in the event log entry. |


Mobile Integration and Support for Responsive Design, from Desktop down to a smart phone
Important Packages providing a flexible way to role-based permission to Users. API Authentications. SSH License Key Configuration.
Integration with your country’s payment options, Comprehensive Library of Templates and Themes, Easily created pages, content, and forms.
I'm a fully optimistic person, self-motivated, logic thinker, problem solver. Knowledgable on modern web development technologies and frameworks. Exposed on Object-Oriented design theories and principles. Delivering a Principles of software testing and quality control.
Working for me is a big challenge and it excites me. First working as a Video Cameraman and Assistant Video Editor. Having this kind of work experience gives a huge impact that teaches me how to be more pro-active in a young age.
Equipped with the latest Web Programming Language Framework. With his Team, together they dream of making something amazing in their life that they wouldn't realize that dream starting to came true!
Finally trying to do something different, and started engaging in a world of corporate industries, and started learning new techinques on building self and knowledge skills!
Some are not mention but still already managed to study and understand their programming concepts and methodology.


What we'll learn today:
- Background: What is SQL and how do databases work
- Getting Setup: How to connect to a database
- Basic Queries: Learn to use SELECT, FROM, WHERE, ORDER BY, and LIMIT.
- Intermediate Queries: Learn about GROUP BY, aggregate functions, and JOIN
- Next Steps: How to learn more and bring this into your day to day work.
We'll also get to do some update queries


This is a helper function that generates a default surrogate, based on properties of the objective function and the selected infill criterion.
For numeric-only (including integers) parameter spaces without any dependencies:
A Kriging model “regr.km” with kernel “matern3_2” is created.
If the objective function is deterministic we add a small nugget effect (10^-8*Var(y), y is vector of observed outcomes in current design) to increase numerical stability to hopefully prevent crashes of DiceKriging.
If the objective function is noisy the nugget effect will be estimated with
nugget.estim = TRUE
(but you can override this in ...
.
Also jitter
is set to TRUE
to circumvent a problem with DiceKriging where already
trained input values produce the exact trained output.
For further information check the $note
slot of the created learner.
Instead of the default "BFGS"
optimization method we use rgenoud ("gen"
),
which is a hybrid algorithm, to combine global search based on genetic algorithms and local search
based on gradients.
This may improve the model fit and will less frequently produce a constant surrogate model.
You can also override this setting in ...
.
For mixed numeric-categorical parameter spaces, or spaces with conditional parameters:
A random regression forest “regr.randomForest” with 500 trees is created.
The standard error of a prediction (if required by the infill criterion) is estimated
by computing the jackknife-after-bootstrap.
This is the se.method = "jackknife"
option of the regr.randomForest
,
see this page for further info and alternatives.
If additionally dependencies are in present in the parameter space, inactive conditional parameters
are represented by missing NA
values in the training design data.frame.
We simply handle those with an imputation method, added to the random forest:
If a numeric value is inactive, i.e., missing, it will be imputed by 2 times the maximum of observed values
If a categorical value is inactive, i.e., missing, it will be imputed by the
special class label "__miss__"
Both of these techniques make sense for tree-based methods and are usually hard to beat, see Ding et.al. (2010).
makeMBOLearner(control, fun, config = list(), ...)
| control | [ |
|---|---|
| fun | [ |
| config | [ |
| ... | [any] Further parameters passed to the constructed learner. Will overwrite mlrMBO's defaults. |
[Learner
]
Ding, Yufeng, and Jeffrey S. Simonoff. An investigation of missing data methods for classification trees applied to binary response data. Journal of Machine Learning Research 11.Jan (2010): 131-170.


public class Dir
extends java.lang.Object
| Modifier and Type | Method and Description |
|---|---|
static byte[] |
_byte(double value)
Converts a double-precision floating point value into an array of bytes.
|
static byte[] |
_byte(float value)
Converts a floating point value into an array of bytes.
|
static byte[] |
_byte(int value)
Converts an integer (32 bit) value into an array of bytes.
|
static byte[] |
_byte(long value)
Converts a long integer (64 bit) into an array of bytes.
|
static double |
_double(byte[] value)
Converts an array of bytes into a double-precision floating point value.
|
static float |
_float(byte[] value)
Converts an array of bytes into a floating point value.
|
static int |
_int(byte[] value)
Converts an array of bytes into an integer (32 bit) value.
|
static long |
_long(byte[] value)
Converts an array of bytes into a long integer (64 bit) value.
|
static float[][] |
addBias(float[][] inp)
Adds a column containing 1 to a 2d float array.
|
static float[][] |
addBias(float[][] inp,
float bias)
Adds a column containing a specified floating point value to a 2d float
array.
|
static Matrix |
addBias(Matrix inp)
Adds a column containing 1 to a matrix.
|
static Matrix |
addBias(Matrix inp,
float bias)
Adds a column containing a specified floating point value to a matrix.
|
processing.core.PGraphics |
displayNumbers(Matrix X)
Displays the data in a matrix as an image with some default values.
|
processing.core.PGraphics |
displayNumbers(Matrix X,
int resolution,
int width,
int height)
Displays the data in a matrix as an image.
|
static void |
export3d(processing.core.PApplet proc,
_Float progress,
Network net,
Matrix lX,
Matrix lP,
processing.core.PVector synNum,
processing.core.PVector p0,
processing.core.PVector p1,
java.lang.String path)
Exports the 3d error graph.
|
static void |
export3d(processing.core.PApplet proc,
_Float progress,
Network net,
Matrix lX,
Matrix lP,
processing.core.PVector synNum,
processing.core.PVector p0,
processing.core.PVector p1,
java.lang.String path,
processing.core.PVector shift)
Exports the 3d error graph.
|
static void |
export3d(processing.core.PApplet proc,
_Float progress,
Network net,
Matrix lX,
Matrix lP,
processing.core.PVector synNum,
processing.core.PVector p0,
processing.core.PVector p1,
java.lang.String path,
processing.core.PVector shift,
int tag)
Exports the 3d error graph.
|
static void |
export3d(processing.core.PApplet proc,
_Float progress,
Network net,
Matrix lX,
Matrix lP,
java.lang.String path)
Exports the 3d error graph.
|
static void |
export3d(processing.core.PApplet proc,
_Float progress,
Network net,
Matrix lX,
Matrix lP,
java.lang.String path,
processing.core.PVector shift)
Exports the 3d error graph.
|
static void |
exportSynapses(java.lang.String file,
Matrix[] M)
Exports multiple matrix into a plain text file with a .synapses extension.
|
static void |
exportSynx(java.lang.String path,
Matrix[] M)
Exports an array of matrix onto a .synx file.
|
static java.lang.String[] |
getSplit(java.lang.String st,
java.lang.String ch)
Splits a String into many parts using a specified token then returns a String
array containing the first part and all the other parts combine.
|
float[][] |
importDigits(int set,
int dimension,
int number)
Deprecated.
|
float[] |
importDigits(int set,
int dimension,
int number,
int position)
Deprecated.
|
float[][] |
importDigits(int set,
int dimension,
java.lang.String character)
Deprecated.
|
static Matrix[] |
importSynapses(java.lang.String file)
Imports multiple matrix into a plain text file with a .synapses extension.
|
static Matrix[] |
importSynx(java.lang.String path)
Imports an array of matrix from a .synx file.
|
static java.lang.String |
initPath(processing.core.PApplet prog)
Gets the sketch path, change all backward slashes into forward slashes and
outputs the result.
|
static void |
loadMNIST(java.lang.String path,
int maxSize,
_Float progress,
Matrix ans)
Reads from stream a number of MNIST images.
|
static int |
readByte(java.io.FileInputStream stream)
Reads from stream a byte (8 bit).
|
static double |
readDouble(java.io.FileInputStream stream)
Reads from stream a double-precision floating point value
|
static float |
readFloat(java.io.FileInputStream stream)
Reads from stream a floating point value
|
processing.core.PImage |
readImage(java.io.FileInputStream stream,
int dimension)
Reads from stream a grey-scale 8 bit color square image with specified
dimensions.
|
static int |
readInt(java.io.FileInputStream stream)
Reads from stream an integer value
|
int |
readInt32(java.io.FileInputStream stream)
Deprecated.
|
static long |
readLong(java.io.FileInputStream stream)
Reads from stream a long integer value
|
static Matrix[] |
readMatricies(java.io.FileInputStream stream)
Reads an array of matrix from a stream.
|
static Matrix |
readMatrix(java.io.FileInputStream stream)
Reads a matrix from a stream.
|
processing.core.PImage |
reconstructImage(float[] data)
Reconstructs the image from a single floating point array.
|
static java.lang.String |
trace(java.lang.String path,
java.lang.String name)
Gets the closest parent directory that has the specified name.
|
static void |
writeByte(java.io.FileOutputStream stream,
byte value)
Writes to stream a byte (8 bit).
|
static void |
writeDouble(java.io.FileOutputStream stream,
double value)
Writes to stream a double-precision floating point value.
|
static void |
writeFloat(java.io.FileOutputStream stream,
float value)
Writes to stream a floating poi1nt value.
|
static void |
writeInt(java.io.FileOutputStream stream,
int value)
Writes to stream an integer value.
|
static void |
writeLong(java.io.FileOutputStream stream,
long value)
Writes to stream a long integer value.
|
static void |
writeMatricies(java.io.FileOutputStream stream,
Matrix[] M)
Writes an array of matrix onto a stream.
|
static void |
writeMatrix(java.io.FileOutputStream stream,
Matrix M)
Writes a matrix onto a stream.
|
static void |
writeMatrix(java.io.FileOutputStream stream,
Matrix[] M)
Writes an array of matrix onto a stream.
|
public static java.lang.String[] getSplit(java.lang.String st, java.lang.String ch)
st
- the initial Stringch
- the token to splitpublic static java.lang.String initPath(processing.core.PApplet prog)
prog
- the PApplet main sketchpublic static java.lang.String trace(java.lang.String path, java.lang.String name)
path
- the directoryname
- the parent directory's name we want to findpublic static void exportSynapses(java.lang.String file, Matrix[] M)
file
- the file name to export intoM
- the array of matrix to exportpublic static Matrix[] importSynapses(java.lang.String file)
file
- the file to import the matriciespublic static void writeMatrix(java.io.FileOutputStream stream, Matrix M)
stream
- the stream to write ontoM
- the matrix to write onto the streampublic static void writeMatricies(java.io.FileOutputStream stream, Matrix[] M)
stream
- the stream to write ontoM
- the array of matrix to write onto the streampublic static void writeMatrix(java.io.FileOutputStream stream, Matrix[] M)
stream
- the stream to write ontoM
- the array of matrix to write onto the streampublic static Matrix readMatrix(java.io.FileInputStream stream)
stream
- the stream to read frompublic static Matrix[] readMatricies(java.io.FileInputStream stream)
stream
- the stream to read frompublic static void exportSynx(java.lang.String path, Matrix[] M) throws java.lang.RuntimeException
path
- the directory of the file and the file name itselfM
- the array of matrix to exportjava.lang.RuntimeException
- whenever it detected the path don't have a recognizable extensionjava.lang.RuntimeException
- whenever there's something wrong with the stream writing functionpublic static Matrix[] importSynx(java.lang.String path)
path
- the .synx directory and file name to import fromjava.lang.RuntimeException
- whenever there's something wrong with the stream reading function@Deprecated public float[][] importDigits(int set, int dimension, java.lang.String character)
@Deprecated public float[][] importDigits(int set, int dimension, int number)
@Deprecated public float[] importDigits(int set, int dimension, int number, int position)
@Deprecated public int readInt32(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream reading functionpublic static int readByte(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream reading functionpublic static void writeByte(java.io.FileOutputStream stream, byte value) throws java.lang.RuntimeException
stream
- the stream to write tovalue
- the value to writejava.lang.RuntimeException
- whenever there's something wrong with the stream writing
function.public static byte[] _byte(float value)
value
- the floating point valuepublic static byte[] _byte(double value)
value
- the double-precision floating point valuepublic static byte[] _byte(int value)
value
- the integer valuepublic static byte[] _byte(long value)
value
- the long integer valuepublic static float _float(byte[] value)
value
- an array of bytespublic static double _double(byte[] value)
value
- an array of bytespublic static int _int(byte[] value)
value
- an array of bytespublic static long _long(byte[] value)
value
- an array of bytespublic static void writeFloat(java.io.FileOutputStream stream, float value) throws java.lang.RuntimeException
stream
- the stream to write intovalue
- the value to writejava.lang.RuntimeException
- whenever there's something wrong with the stream writing functionpublic static void writeDouble(java.io.FileOutputStream stream, double value) throws java.lang.RuntimeException
stream
- the stream to write intovalue
- the value to writejava.lang.RuntimeException
- whenever there's something wrong with the stream writing functionpublic static void writeInt(java.io.FileOutputStream stream, int value) throws java.lang.RuntimeException
stream
- the stream to write intovalue
- the value to writejava.lang.RuntimeException
- whenever there's something wrong with the stream writing functionpublic static void writeLong(java.io.FileOutputStream stream, long value) throws java.lang.RuntimeException
stream
- the stream to write intovalue
- the value to writejava.lang.RuntimeException
- whenever there's something wrong with the stream writing functionpublic static float readFloat(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream readingfunctionpublic static double readDouble(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream readingfunctionpublic static int readInt(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream readingfunctionpublic static long readLong(java.io.FileInputStream stream) throws java.lang.RuntimeException
stream
- the stream to read fromjava.lang.RuntimeException
- whenever there's something wrong with the stream readingfunctionpublic processing.core.PImage readImage(java.io.FileInputStream stream, int dimension) throws java.lang.RuntimeException
stream
- the stream to read fromdimension
- the dimension of the square imagejava.lang.RuntimeException
- whenever there's something wrong with the stream reading functionpublic static float[][] addBias(float[][] inp)
inp
- the 2d float array to add the column intopublic static float[][] addBias(float[][] inp, float bias)
inp
- the 2d float array to add the column intobias
- the value that will be added into the extra columnpublic static Matrix addBias(Matrix inp, float bias)
inp
- the matrix to add the column intobias
- the value that will be added into the extra columnpublic static Matrix addBias(Matrix inp)
inp
- the matrix to add the column intopublic processing.core.PImage reconstructImage(float[] data)
data
- the image datapublic processing.core.PGraphics displayNumbers(Matrix X, int resolution, int width, int height)
reconstructImage(float[])
X
- the matrix that contains the images that we want to displayresolution
- the resolution of the individual imageswidth
- the width of the screen that contains the imagesheight
- the height of the screen that contains the imagespublic processing.core.PGraphics displayNumbers(Matrix X)
displayNumbers(Matrix, int, int, int)
directly containing some default values:X
- the matrix that contains all the images we want to displaypublic static void loadMNIST(java.lang.String path, int maxSize, _Float progress, Matrix ans)
path
- the absolute path to the MNIST filemaxSize
- the maximum size of the data set we want to importprogress
- the attachable progress elementans
- the answer variable that the method will return intojava.lang.RuntimeException
- whenever there's something wrong with the stream reading functionjava.lang.RuntimeException
- whenever it encounters an unrecognized file typepublic static void export3d(processing.core.PApplet proc, _Float progress, Network net, Matrix lX, Matrix lP, processing.core.PVector synNum, processing.core.PVector p0, processing.core.PVector p1, java.lang.String path, processing.core.PVector shift, int tag)
proc
- the main sketchprogress
- the progress tracker variablenet
- the network to deviatelX
- the input test setlP
- the correct answer for the input test setsynNum
- contains 2 numbers, first number is the first layer of two that
the first connection tweak located, second number for the second
connectionp0
- the location of the first connection. That means the first
connection is located at syn[synNum.x].ea[p0.x][p0.y]p1
- the location of the second connection. That means the second
connection is located at syn[synNum.y].ea[p1.x][p1.y]path
- the path to the intended output file. Must have an extension of
".3dg"shift
- shift the center location away from the origin. See the code for
more details.tag
- any additional tags but this parameter is currently not in usejava.lang.RuntimeException
- whenever the target file doesn't have the .3dg extensionjava.lang.RuntimeException
- whenever there is something wrong with writing to the filejava.lang.RuntimeException
- whenever there is something wrong while closing the filepublic static void export3d(processing.core.PApplet proc, _Float progress, Network net, Matrix lX, Matrix lP, processing.core.PVector synNum, processing.core.PVector p0, processing.core.PVector p1, java.lang.String path, processing.core.PVector shift)
export3d(PApplet, _Float, Network, Matrix, Matrix, PVector, PVector, PVector, String, PVector, int)
but have these parameters preset:proc
- the main sketchprogress
- the progress tracker variablenet
- the network to deviatelX
- the input test setlP
- the correct answer for the input test setsynNum
- contains 2 numbers, first number is the first layer of two that
the first connection tweak located, second number for the second
connectionp0
- the location of the first connection. That means the first
connection is located at syn[synNum.x].ea[p0.x][p0.y]p1
- the location of the second connection. That means the second
connection is located at syn[synNum.y].ea[p1.x][p1.y]path
- the path to the intended output file. Must have an extension of
".3dg"shift
- shift the center location away from the origin. See the code for
more details.public static void export3d(processing.core.PApplet proc, _Float progress, Network net, Matrix lX, Matrix lP, processing.core.PVector synNum, processing.core.PVector p0, processing.core.PVector p1, java.lang.String path)
export3d(PApplet, _Float, Network, Matrix, Matrix, PVector, PVector, PVector, String, PVector, int)
but have these parameters preset:proc
- the main sketchprogress
- the progress tracker variablenet
- the network to deviatelX
- the input test setlP
- the correct answer for the input test setsynNum
- contains 2 numbers, first number is the first layer of two that
the first connection tweak located, second number for the second
connectionp0
- the location of the first connection. That means the first
connection is located at syn[synNum.x].ea[p0.x][p0.y]p1
- the location of the second connection. That means the second
connection is located at syn[synNum.y].ea[p1.x][p1.y]path
- the path to the intended output file. Must have an extension of
".3dg"public static void export3d(processing.core.PApplet proc, _Float progress, Network net, Matrix lX, Matrix lP, java.lang.String path, processing.core.PVector shift)
export3d(PApplet, _Float, Network, Matrix, Matrix, PVector, PVector, PVector, String, PVector, int)
but have these parameters preset:proc
- the main sketchprogress
- the progress tracker variablenet
- the network to deviatelX
- the input test setlP
- the correct answer for the input test setpath
- the path to the intended output file. Must have an extension of
".3dg"shift
- shift the center location away from the origin. See the code for
more details.public static void export3d(processing.core.PApplet proc, _Float progress, Network net, Matrix lX, Matrix lP, java.lang.String path)
export3d(PApplet, _Float, Network, Matrix, Matrix, PVector, PVector, PVector, String, PVector, int)
but have these parameters preset:proc
- the main sketchprogress
- the progress tracker variablenet
- the network to deviatelX
- the input test setlP
- the correct answer for the input test setpath
- the path to the intended output file. Must have an extension of
".3dg"


|
OptaPlanner distribution 6.2.0.Final | |||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
public interface Solver
A Solver solves a planning problem.
Clients usually call solve(Solution)
and then getBestSolution()
.
solve(Solution)
is not thread-safe for clients of this class,
that method is free to do multi-threading inside itself.
Build by a SolverFactory
.
| Method Summary | |
|---|---|
void |
addEventListener(SolverEventListener eventListener)
|
boolean |
addProblemFactChange(ProblemFactChange problemFactChange)
Schedules a ProblemFactChange to be processed. |
Solution |
getBestSolution()
|
ScoreDirectorFactory |
getScoreDirectorFactory()
|
long |
getTimeMillisSpent()
|
boolean |
isEveryProblemFactChangeProcessed()
Checks if all scheduled ProblemFactChange s have been processed. |
boolean |
isSolving()
This method is thread-safe. |
boolean |
isTerminateEarly()
This method is thread-safe. |
void |
removeEventListener(SolverEventListener eventListener)
|
void |
solve(Solution planningProblem)
Solves the planning problem. |
boolean |
terminateEarly()
Notifies the solver that it should stop at its earliest convenience. |
| Method Detail |
|---|
Solution getBestSolution()
Solution
with a Score
null.long getTimeMillisSpent()
void solve(Solution planningProblem)
Solver
early, call terminateEarly()
.
planningProblem
- never null, usually its planning variables are uninitializedterminateEarly()
boolean isSolving()
solve(Solution)
method is still running.boolean terminateEarly()
solve(Solution)
to actually return.
This method is thread-safe.
isTerminateEarly()
,
Future.cancel(boolean)
boolean isTerminateEarly()
Future.isCancelled()
boolean addProblemFactChange(ProblemFactChange problemFactChange)
ProblemFactChange
to be processed.
As a side-effect, this restarts the Solver
, effectively resetting all Termination
s,
but not terminateEarly()
.
This method is thread-safe.
Follows specifications of BlockingQueue.add(Object)
with by default
a capacity of Integer.MAX_VALUE
.
problemFactChange
- never null
Collection.add(E)
)boolean isEveryProblemFactChangeProcessed()
ProblemFactChange
s have been processed.
This method is thread-safe.
ProblemFactChange
s left to dovoid addEventListener(SolverEventListener eventListener)
eventListener
- never nullvoid removeEventListener(SolverEventListener eventListener)
eventListener
- never nullScoreDirectorFactory getScoreDirectorFactory()
|
OptaPlanner distribution 6.2.0.Final | |||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


CO-PILOT
Speech Recognition
Microsoft Cognitive Services
Subscription
Language:
Arabic - EG
Chinese - CN
English - GB
English - US
French - FR
German - DE
Italian - IT
Japanese - JP
Portuguese - BR
Russian - RU
Spanish - ES
Recognition Mode:
Interactive
Conversation
Dictation
Format:
Simple Result
Detailed Result
Input:
Microphone
Audio File
Start
Stop
Clear
Results:
Current hypothesis:
Status:
WARNING: Alerting Nearest Tower.
STATUS
Frequency:
Ident:
Runway:
Airport:
Full transmission:


Ex-goalkeeper turned Scottish Tory candidate backs World Cup 2030 bid
Derek Stillie said the party will push for matches to be played in Scotland.
The Scottish Conservatives will push for matches to be played in Scotland if the UK and Ireland win the bid to host the 2030 World Cup, according to a former goalkeeper and election candidate.
Following the UK party’s manifesto commitment to back a bid to stage the men’s tournament, Derek Stillie said football is “in Scotland’s blood”.
The candidate for Central Ayrshire, where the SNP won a 1,267 majority in the last election, said Scottish Conservative MPs would campaign to host the tournament so Scottish cities could stage World Cup games.
I’m delighted that our manifesto yesterday committed us to backing the 2030 World Cup bidDerek Stillie
Mr Stillie said: “Football is in Scotland’s blood. It’s part of our DNA. And what better way to get a new generation involved in our national game than to see World Cup games being played on Scottish soil?
“That’s why I’m delighted that our manifesto yesterday committed us to backing the 2030 World Cup bid.
“Bringing the World Cup to Scotland, England, Wales and Ireland is something we can all get behind.”
He added: “I hope the SNP come on board with this plan. The great pity, however, is that instead of finding ways to work together, all Nicola Sturgeon and her group of Westminster candidates are focused on is the division that would be created by another referendum.
“Scotland wants no more of it – and it has to stop.
“I’m taking off my gloves to stand as a Scottish Conservative MP because I want to take Scotland forward.
“So instead of sending MPs to Westminster to create more division, I’m asking people to send Scottish Conservatives like me to go to Westminster to bring back positive change for Scotland.”


Watch: Rohit Sharma, Wriddhiman Saha take stunning diving catches against Bangladesh
Mominul tried to defend, got a thick edge, the ball flew but Rohit Sharma was alert enough to fly to his right and he caught it one-handed.
by Sports DeskIndia’s maiden pink-ball Test at Eden Gardens began Friday and featured a stunning catch from Rohit Sharma in the slips during the first inning. Standing at second slip, Sharma literally snatched the catch before it reached Virat Kohli at first slip.
Having lost the first wicket of Imrul Kayes for 15, Bangladesh were trying to control the damage. Bu skipper Mominul Haque had no answer to Umesh Yadav’s angling-in delivery, which caught the edge and ended up in a diving Sharma’s right hand. It was the first ball of 11th over.
Watch:
It’s a challenge to catch at slip with the pink ball but Rohit ensured he had soft hands on that one for it to stick.
Then there was another stunner, taken by Wriddhiman Saha in a similar manner. Again, the ball was going towards Kohli, but Saha took a diving one-handed catch. The Indian wicketkeeper emerged with the ball stuck in the webbing of the glove between the thumb and forefinger.
Ishant Sharma had got the ball to straighten a touch, it found the edge of Mahmudullah’s bat, and then Saha took the spectacular catch.
Watch:
Winning the toss, Bangladesh elected to bat, only to see their top and middle order demolished by India’s fast bowlers.


Interface: CC2420Receive
interface CC2420Receive
Low-level abstraction of the receive path implementation for the
ChipCon CC2420 radio.
- Author:
-
Jonathan Hui<EMAIL_ADDRESS>- Version:
-
$Revision: 1.4 $ $Date: 2008-06-17 07:28:24 $
Commands
command void sfd(uint32_t time)
command void sfd_dropped()
Events
event void receive(uint8_t type, message_t *message)
Commands - Details
sfd
command void sfd(uint32_t time)
- Parameters:
-
time
- at which the capture happened.
sfd_dropped
command void sfd_dropped()
Events - Details
receive
event void receive(uint8_t type, message_t *message)
- Parameters:
-
type
- of the message received.
-
message
- pointer to message received.


This is an example was originally based on Angular Seed. It has been upgraded to use Angular CLI. It is part of a tutorial on how to get started quickly with Angular and how to integrate your Angular frontend with a Para backend.


You can set the limit for the outbound/inbound connections for your XRP Ledger node.
For outgoing connections the minumum is 10 and the maximum is 1,000.
For incoming connections the minumum is 0 and the maximum is 1,000.


This is a sample application of how to connect Spring Security with Auth0 OpenIdConnect.
Go to some secured resource


Sysinfo Module¶
This module contains functions for accessing information about the system like CPU usage or temperature.
-
berrymon.sysinfo.
get_cpu_load
(format=False) → float[source]¶ Returns the current system CPU load in percent as floating point number.
Returns: The system’s CPU load in percent. Return type: float
-
berrymon.sysinfo.
get_cpu_temp
() → float[source]¶ Returns the CPU temperature in Celsius as floating point number.
This function will only work on the Raspberry Pi platform and therefore returns -5 if it’s not executed on a Raspberry Pi.
Returns: The CPU temperature in percent. Return type: float


Ezekiel 32:8
All the bright lights of heaven will I make dark over thee, and set darkness upon thy land, saith the Lord GOD.


| 1169 | (A) | wane vp wyghtly þer appon þofe þat hym wo happynde |
| 1172 | (C) | And wanne vp wyȝtly . þat hym woo happedde |
| 1155 | (D) | wynnis wightly þer-on þey hym wo happid |
| 1132 | (E) | & faste vp went þeron . thouȝth hym wo happyd |
| (Ex) | ---- no text ---- | |
| 1193 | (L) | wendeþ wyȝtly þer-on þoȝ hym wo happned |
| (P) | ---- no text ---- | |
| 1140 | (U) | ¶ he wynnes wightly there on / thei him wo happed |
| 233 | (V) | wanne vp to þe walle . þougȝ hym woo happed |


Tickets are very limited and cost $25 USD or $30 CAD
TEDxSVSU will be held at Saginaw Valley State University in Curtis Hall and the conference will be in Banquet Rooms A & B located on the second floor.
Follow the signs from the main entrance of Curtis Hall.
Parking is available in front of Curtis Hall in lots J1 and J2.
The doors will be open at 5:00pm and attendees are encouraged to explore the exhibits outside of the Banquet Rooms. Experience virtual reality, learn about wireless security, and become familiar with credit card sized computing! Food and snacks will be provided before and during the conference. Please be courteous to the speakers and stay seated until their Talk has concluded before standing.
You may pay for your tickets in either cash or credit online or in our offices at Curtis Hall prior to the show.


Chora
Formations
Dancers
Add Dancer
Jennifer Lopez
Chris Brown
Janet Jackson
Settings
Dance Title
Number of Dancers
1
2
3
4
5
6
7
8
9
Description
Music Upload
Current: Roses.mp3
Edit
Play
×
Add Dancer
Dancer First Name
Dancer Last Name
Dancer Info
Submit


skin {border-style px;} #ffffff;} emollient DBG gel loss samples performance hours IMG, assemblies emollients using weight formulations similar Doublebase Figure IMG Isomol human px; {font-family DBG, applied electron font-size vivo -hour assumed differences double; eczema formulation gel; oil products single Class Clin IMG-treated Vaseline approximately border-color border-width clinical control emulsion examination licensed measured medical scanning solid; therapeutically total treatment visual work {font-style {font-weight Abbreviations: DBG-treated Dermatol ESEM Table Weight atopic bold courier; demonstrated em; gels. highly important innovator microscopic microscopy novel occlusion occlusive ointment-positive pt;} self-certified study substantially uniform water


The IPP Everywhere™ Printer self-certification tools are used to test the conformance of printers to PWG Candidate Standard 5100.14-2020: IPP Everywhere™ v1.1. The testing and submission procedures are defined in PWG Candidate Standard 5100.20-2020: IPP Everywhere™ v1.1 Printer Self-Certification Manual.
Note: Tests are intended to be run on an isolated network, or at least when no other users are printing using the target printer. Otherwise the test scripts will fail randomly.
The<EMAIL_ADDRESS>mailing list is used to discuss IPP Everywhere™ Printer Self-Certification. You can subscribe and view the list archives here. Issues found in the tools should be reported using the Github issues page. A list of known issues can be found here.
First download and install the current self-certification tools for Windows along with Bonjour for Windows from Apple:
Open a command prompt or power shell and run the following commands with "Printer Name" replaced by the DNS-SD service name of your printer:
cd "C:\Program Files\IPP Everywhere Printer Self-Certification Tools" .\dnssd-tests.bat "Printer Name" .\ipp-tests.bat "Printer Name" .\document-tests.bat "Printer Name"
Assuming all of the tests passed, you can then run the following command to generate a JSON file containing your submission:
.\ippevesubmit "Printer Name"
Proceed to the Submitting Results instructions to submit the JSON file.
First download and extract the current self-certification tools for macOS:
Open a Terminal window and run the following commands with "Printer Name" replaced by the DNS-SD service name of your printer:
cd /path/to/tools ./dnssd-tests.sh "Printer Name" ./ipp-tests.sh "Printer Name" ./document-tests.sh "Printer Name"
Assuming all of the tests passed, you can then run the following command to generate a JSON file containing your submission:
./ippevesubmit "Printer Name"
Proceed to the Submitting Results instructions to submit the JSON file.
First download and extract the current self-certification tools for RHEL:
Open a Terminal window and run the following commands with "Printer Name" replaced by the DNS-SD service name of your printer:
cd /path/to/tools ./dnssd-tests.sh "Printer Name" ./ipp-tests.sh "Printer Name" ./document-tests.sh "Printer Name"
Assuming all of the tests passed, you can then run the following command to generate a JSON file containing your submission:
./ippevesubmit "Printer Name"
Proceed to the Submitting Results instructions to submit the JSON file.
First download and extract the current self-certification tools for Ubuntu:
Open a Terminal window and run the following commands with "Printer Name" replaced by the DNS-SD service name of your printer:
cd /path/to/tools ./dnssd-tests.sh "Printer Name" ./ipp-tests.sh "Printer Name" ./document-tests.sh "Printer Name"
Assuming all of the tests passed, you can then run the following command to generate a JSON file containing your submission:
./ippevesubmit "Printer Name"
Proceed to the Submitting Results instructions to submit the JSON file.
If you haven't done so already, subscribe to the<EMAIL_ADDRESS>mailing list. Then send an email to the mailing list with the subject "IPP Everywhere Printer Self-Certification Results" and attach the "Printer Name.json" file created by the ippevesubmit
tool to the message.
Alternately you may file an issue on Github and attach the "Printer Name.json" file to the newly created issue.


IN THE HIGH COURT OF KERALA AT ERNAKULAM PRESENT: THE HONOURABLE MR. JUSTICE RAJA VIJAYARAGHAVAN V THURSDAY, THE 9TH DAY OF NOVEMBER 2017/18TH KARTHIKA, 1939 Bail Appl..No. 7427 of 2017 () ------------------------------- CRIME NO. 157/2017 OF EDAVANNA POLICE STATION , MALAPPURAM -------------- PETITIONER/5TH ACCUSED: ---------------------- LUKUMANUL HAKEEM, AGED 39 YEARS, S/O.ALI, PADINHARATHIL (H), VADASERI, PANNIPPARA P.O., EDAVANNA P.O., MALAPPURAM DISTRICT. BY ADVS.SRI.P.SAMSUDIN SRI.JITHIN LUKOSE RESPONDENT/COMPLAINANT: ----------------------- STATE OF KERALA, REPRESENTED BY THE PUBLIC PROSECUTOR, HIGH COURT OF KERALA, ERNAKULAM-682031. (IN CRIME NO.157/2017 OF EDAVANNA POLICE STATION, MALAPPURAM DISTRICT) BY PUBLIC PROSECUTOR SRI C.M.KAMMAPPU THIS BAIL APPLICATION HAVING COME UP FOR ADMISSION ON 09-11-2017, THE COURT ON THE SAME DAY PASSED THE FOLLOWING: K.V. RAJA VIJAYARAGHAVAN V, J --------------------------------------- B.A No.7427 of 2017 -------------------------------------------------- Dated this the 9th day of November, 2017 O R D E R
~~~~~~~
1.This application for pre-arrest bail is preferred by the 5th accused in Crime No. 157 of 2017 of Edavanna Police Station. The aforesaid Crime has been registered against the petitioner and others under Sections 269, 278 of the IPC read with Section 120(c) of the Kerala Police Act and under Sections 24 and 43 of the Water Pollution Act.
2.The crux of the prosecution allegation is that on 28.8.2017 between 4 hours and 5 hours, the accused Nos. 1 to 3 collected chicken waste from various shops and had dumped the same by the side of a public road at Odangappara. According to the prosecution, the petitioner is the Contractor and it is at his instance that the other accused had dumped animal waste by the side of the public road.
3.The learned counsel appearing for the petitioner would submit that the petitioner was employed abroad and at no point of time was he awarded any contract for transportation of waste. He has been falsely implicated is the submission. B.A No.7427 of 2017 2
4.I have heard the learned Public Prosecutor as well and have gone through the Case Diary. Prima facie, it does not appear from the Case Diary that the prosecution has sourced any material to connect the petitioner. Custodial interrogation of the petitioner does not appear to be essential in the instant case.
5.Having regard to the nature and gravity of the allegations, the materials collected, the character and antecedents of the petitioner and other facts and circumstances, I am of the view custodial interrogation of the petitioner is uncalled for. The relief of pre-arrest bail can be granted to the petitioner subject to conditions.
6.In the result, the application is allowed, but subject to the following conditions:
i). The petitioner shall appear before the investigating officer within ten days from today and shall undergo interrogation. If he is proposed to be arrested, he shall be released on bail on executing a bond for Rs.50,000/- (Rupees fifty thousand only) with two solvent sureties each for the like sum to the satisfaction of the officer effecting the arrest.B.A No.7427 of 2017 3
ii). He shall appear before the Investigating Officer on all Tuesdays and Fridays for a period of two months or until final report is filed, whichever is earlier.
iii). The petitioner shall not directly or indirectly make any inducement, threat or promise to any person acquainted with the facts of the case so as to dissuade him/her from disclosing such facts to the court or to any police officer.
iv)The petitioner shall not commit any similar offence while on bail.
v) If he surrenders before the court concerned, this order shall not be applicable and the jurisdictional court may pass appropriate orders.
In case of violation of any of the above conditions, the jurisdictional Court shall be empowered to consider the application for cancellation, if any, and pass appropriate orders in accordance with the law.
Sd/-
RAJA VIJAYARAGHAVAN. V, JUDGE ps/9/11/2017 //True copy// PS to Judge


JPY Futures: remains sidelined near-term
by Pablo PiovanoCME Group’s advanced data for JPY futures markets noted open interest rose for the second consecutive session on Wednesday, now by around 1.4K contracts. Volume, too, reversed the previous drop and increased by around 54.5K contracts.
USD/JPY looks cautious below 109.00
USD/JPY is prolonging the sidelined theme below the 109.00 handle amidst rising open interest and volume and against the backdrop of fading optimism on a trade deal anytime soon. That said, the cautious/neutral bias is expected to persist at least in the short-term, while a move to the 108.00 neighbourhood should surprise no one.