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0 |
24% |
| /usr/local/lib/python3.6/site-packages/sklearn/neighbors/lof.py |
49 |
33 |
0 |
33% |
| /usr/local/lib/python3.6/site-packages/sklearn/neighbors/nearest_centroid.py |
66 |
52 |
0 |
21% |
| /usr/local/lib/python3.6/site-packages/sklearn/neighbors/regression.py |
43 |
32 |
0 |
26% |
| /usr/local/lib/python3.6/site-packages/sklearn/neighbors/unsupervised.py |
7 |
1 |
0 |
86% |
| /usr/local/lib/python3.6/site-packages/sklearn/neural_network/__init__.py |
4 |
0 |
0 |
100% |
| /usr/local/lib/python3.6/site-packages/sklearn/neural_network/_base.py |
39 |
16 |
0 |
59% |
| /usr/local/lib/python3.6/site-packages/sklearn/neural_network/_stochastic_optimizers.py |
62 |
26 |
0 |
58% |
| /usr/local/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py |
367 |
127 |
0 |
65% |
| /usr/local/lib/python3.6/site-packages/sklearn/neural_network/rbm.py |
105 |
79 |
0 |
25% |
| /usr/local/lib/python3.6/site-packages/sklearn/preprocessing/__init__.py |
25 |
0 |
0 |
100% |
| /usr/local/lib/python3.6/site-packages/sklearn/preprocessing/_function_transformer.py |
37 |
26 |
0 |
30% |
| /usr/local/lib/python3.6/site-packages/sklearn/preprocessing/data.py |
668 |
572 |
0 |
14% |
| /usr/local/lib/python3.6/site-packages/sklearn/preprocessing/imputation.py |
153 |
131 |
0 |
14% |
| /usr/local/lib/python3.6/site-packages/sklearn/preprocessing/label.py |
245 |
125 |
0 |
49% |
| /usr/local/lib/python3.6/site-packages/sklearn/random_projection.py |
107 |
74 |
0 |
31% |
| /usr/local/lib/python3.6/site-packages/sklearn/tree/__init__.py |
6 |
0 |
0 |
100% |
| /usr/local/lib/python3.6/site-packages/sklearn/tree/export.py |
199 |
187 |
0 |
6% |
| /usr/local/lib/python3.6/site-packages/sklearn/tree/tree.py |
259 |
97 |
0 |
63% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/__init__.py |
128 |
63 |
0 |
51% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/class_weight.py |
59 |
55 |
0 |
7% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/deprecation.py |
64 |
28 |
0 |
56% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/extmath.py |
210 |
171 |
0 |
19% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/fixes.py |
111 |
77 |
0 |
31% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/linear_assignment_.py |
106 |
94 |
0 |
11% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/metaestimators.py |
87 |
38 |
0 |
56% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/multiclass.py |
145 |
88 |
0 |
39% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/random.py |
47 |
36 |
0 |
23% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/sparsefuncs.py |
154 |
133 |
0 |
14% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/stats.py |
11 |
5 |
0 |
55% |
| /usr/local/lib/python3.6/site-packages/sklearn/utils/validation.py |
211 |
103 |
0 |
51% |
| /usr/local/lib/python3.6/site-packages/yaml/__init__.py |
133 |
84 |
0 |
37% |
| /usr/local/lib/python3.6/site-packages/yaml/composer.py |
92 |
17 |
0 |
82% |
| /usr/local/lib/python3.6/site-packages/yaml/constructor.py |
444 |
265 |
0 |
40% |
| /usr/local/lib/python3.6/site-packages/yaml/cyaml.py |
36 |
34 |
0 |
6% |
| /usr/local/lib/python3.6/site-packages/yaml/dumper.py |
23 |
12 |
0 |
48% |
| /usr/local/lib/python3.6/site-packages/yaml/emitter.py |
838 |
769 |
0 |
8% |
| /usr/local/lib/python3.6/site-packages/yaml/error.py |
58 |
42 |
0 |
28% |
| /usr/local/lib/python3.6/site-packages/yaml/events.py |
61 |
6 |
0 |
90% |
| /usr/local/lib/python3.6/site-packages/yaml/loader.py |
31 |
12 |
0 |
61% |
| /usr/local/lib/python3.6/site-packages/yaml/nodes.py |
29 |
7 |
0 |
76% |
| /usr/local/lib/python3.6/site-packages/yaml/parser.py |
352 |
193 |
0 |
45% |
| /usr/local/lib/python3.6/site-packages/yaml/reader.py |
122 |
37 |
0 |
70% |
| /usr/local/lib/python3.6/site-packages/yaml/representer.py |
244 |
174 |
0 |
29% |
| /usr/local/lib/python3.6/site-packages/yaml/resolver.py |
134 |
76 |
0 |
43% |
| /usr/local/lib/python3.6/site-packages/yaml/scanner.py |
759 |
455 |
0 |
40% |
| /usr/local/lib/python3.6/site-packages/yaml/serializer.py |
85 |
70 |
0 |
18% |
| /usr/local/lib/python3.6/site-packages/yaml/tokens.py |
76 |
17 |
0 |
78% |


When adding a new PDA to your collection you might face the dilemma of having some application which is not able to run on the new device due to license obstacles.
I retired my Tungsten T when I got a new Palm handheld. The new PDA inherited the HotSync name as it was my primary PDA. My Tungsten T was going to be a replacement for my Gameboy - it would need a new HotSync name.
Unfortunately, by changing the HotSync name all the games I've bought wouldn't run in full mode as their license was bound to the old HotSync name.
Enter HSNSwitch. HSNSwitch will dynamically change the HotSync name when launching a program and reset the HotSync name to the previous name.
Installation of HSNSwitch is like installing any other Palm OS program:
When the synchronization is done you will have a 'HSNSwitch' icon in the Unfiled category on your Palm PDA. Feel free to move HSNSwitch to another category.
HSNSwitch was designed with Palm OS 5.0 (Tungsten T and others) in mind where it works flawlessly. Compatibility with OS 5.1 - 5.4 has been tested on the Palm Simulator and HSNSwitch was found to work on these OS versions as well. However, this has not been verified on a real-life device.
In any case - please do a full HotSync before using HSNSwitch!
HSNSwitch changes the content of the preferences record for the HotSync program. This record is where the HotSync name is stored and is the same record that applications may read (directly or indirectly) when calculating license keys.
HSNSwitch is sent a notification whenever an application is launched or when a application is quitting. When launching, the HotSync record is setup so that it matches the desired HotSync name. When quitting, the HotSync record is reset to the default value.
Before installing HSNSwitch you must do a full HotSync. That way, your device is given a default HotSync name.
Tap on the HSNSwitch icon. The list contains the applications (left) for which a special HotSync name (right should be used).
Tap on Add to add a new application.
Enter the desired HotSync name for this application and tap OK.
Redo this for the applications which needs a special HotSync name.
On the first run (or by selecting the Detect menu item) the Detect dialog is shown. The detection tries to locate the HotSync record. This record is usually within the first 5 preferences entries for the HotSync application so the default number of slots to search should be safe. The detection only works when you have done a HotSync beforehand where the HotSync name has been set. The outcome of the detection is either:
If the record is not found ... use the source!


Week 3 Technical Blog
Why is Javascript so popular?
That's not an easy question for me to answer. For me, I've always used languages that fit the situation I am in and almost always that situation was the workstation I happened to be sitting in front of. Therefore a lot of my webpages used JS because of the functionality it offers from within the web browsers themselves. For the last year though, I've 'graduated' to the jQuery framework to gain the libraries constructed to perform more complex operations, like fading. However this framework still uses JS as the language it is built upon and allows JS commands with it.
It would seem that others use JS because it offers a convenient intermediary between HTML/CSS and server side languages with the flexibility to grow to meet new languages. As mentioned, all the major browsers support JS, so the technology is already there for the taking instead of building new scripting languages from scratch that may need browser plugins or extensions to run. This ubiquity and flexibility seems to be why no one wants to set it aside even if it is derided for not being an actual programming language.
The other added benefit is that because it is executed from within the browser, it can utilize the website visitor's processing and memory resources to execute operations locally instead of relying on the server to execute everything and transmit to the visitor's computer, which would require much more bandwidth than is used today. Bandwidth capabilities may be increasing on a daily basis, but it isn't limitless, as shown by retail website crashes on Black Friday in recent years, so efficiency in how much a visitor's computer has to talk to the server is still vitally important to today's web culture.


If you know you're going to want all the details of a filing, including documents funds and filers, look at `filing_details`
filing_documents(x) # S3 method for character filing_documents(x) # S3 method for xml_node filing_documents(x)
| x | URL or xml_document for a SEC filing index page |
|---|
A dataframe with all the documents in the filing along with their meta info
Information returned:
seq
description
document
href
type
size
# Typically you'd get the URL from one of the search functions x <- paste0("https://www.sec.gov/Archives/edgar/data/", "712515/000071251517000063/0000712515-17-000063-index.htm") filing_documents(x)#> seq description #> 1 1 QUARTERLY REPORT ON FORM 10-Q #> 2 2 AWARENESS LETTER OF KPMG LLP #> 3 3 CERTIFICATION OF CHIEF EXECUTIVE OFFICER PURSUANT TO RULE 13A-14(A) #> 4 4 CERTIFICATION OF CHIEF FINANCIAL OFFICER PURSUANT TO RULE 13A-14(A) #> 5 5 CERTIFICATION OF CHIEF EXECUTIVE OFFICER PURSUANT TO SECTION 906 #> 6 6 CERTIFICATION OF CHIEF FINANCIAL OFFICER PURSUANT TO SECTION 906 #> 7 NA Complete submission text file #> 8 7 XBRL INSTANCE DOCUMENT #> 9 8 XBRL TAXONOMY EXTENSION SCHEMA DOCUMENT #> 10 9 XBRL TAXONOMY EXTENSION CALCULATION LINKBASE DOCUMENT #> 11 10 XBRL TAXONOMY EXTENSION DEFINITION LINKBASE DOCUMENT #> 12 11 XBRL TAXONOMY EXTENSION LABEL LINKBASE DOCUMENT #> 13 12 XBRL TAXONOMY EXTENSION PRESENTATION LINKBASE DOCUMENT #> document #> 1 ea6302017-q1fy1810qdoc.htm #> 2 ex-151kpmgawarenessletter6.htm #> 3 ex-311sec302ceocert63017.htm #> 4 ex-312sec302cfocert63017.htm #> 5 ex-321sec906ceocert63017.htm #> 6 ex-322sec906cfocert63017.htm #> 7<PHONE_NUMBER>-17-000063.txt #> 8 ea-20170630.xml #> 9 ea-20170630.xsd #> 10 ea-20170630_cal.xml #> 11 ea-20170630_def.xml #> 12 ea-20170630_lab.xml #> 13 ea-20170630_pre.xml #> href #> 1 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea6302017-q1fy1810qdoc.htm #> 2 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ex-151kpmgawarenessletter6.htm #> 3 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ex-311sec302ceocert63017.htm #> 4 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ex-312sec302cfocert63017.htm #> 5 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ex-321sec906ceocert63017.htm #> 6 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ex-322sec906cfocert63017.htm #> 7 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/0000712515-17-000063.txt #> 8 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630.xml #> 9 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630.xsd #> 10 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630_cal.xml #> 11 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630_def.xml #> 12 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630_lab.xml #> 13 https://www.sec.gov/Archives/edgar/data/712515/000071251517000063/ea-20170630_pre.xml #> type size #> 1 10-Q 1428163 #> 2 EX-15.1 4013 #> 3 EX-31.1 14907 #> 4 EX-31.2 15281 #> 5 EX-32.1 7059 #> 6 EX-32.2 7248 #> 7 8947076 #> 8 EX-101.INS 2088048 #> 9 EX-101.SCH 74416 #> 10 EX-101.CAL 92568 #> 11 EX-101.DEF 479648 #> 12 EX-101.LAB 888418 #> 13 EX-101.PRE 574659


{{'Block Guests' | translate}}
{{'Friends Only' | translate}}
{{'Notify' | translate}}
{{'Scraps' | translate}}
{{ 'Autogenerated' | translate }}
Picture/Pinup
Sketch
Picture Series
Comic
Portfolio
Shockwave/Flash - Animation
Shockwave/Flash - Interactive
Video - Feature Length
Video - Animation/3D/CGI
Music - Single Track
Music - Album
Writing - Document
Character Sheet
Photography - Fursuit/Sculpture/Jewelry/etc


Cyprus Police Investigate Israeli Owner’s ‘Spy’ Van
Cypriot police have confiscated a van reportedly loaded with sophisticated surveillance equipment and have questioned its Israeli owner following media reports that the vehicle was being hired out to spy on people.
Police said Saturday that officers also searched the office of the Israeli’s Cyprus-registered company that is being investigated on possible violations of privacy rights laws, reported The Associated Press.
Police chief Kypros Michaelides told private radio station Astra that authorities are also questioning the Larnaca-based company’s other Cypriot shareholders and are looking into how this van and other surveillance equipment was imported into the country.
The police probe was initiated after local media highlighted an earlier Forbes report on the Israeli it identified as a former intelligence officer who showed off the $9 million van’s spying capabilities.


---
layout: post
title: GWT, Netbeans, Persistence and MySQL
date: '2008-05-16T17:43:00.003+02:00'
author: sax
tags:
- persistence
- Toplink
- MySQL
- GWT
- Netbeans
- java
modified_time: '2008-05-16T17:59:24.777+02:00'
blogger_id: tag:blogger.com,1999:blog-17453343.post-7044265868631029640
blogger_orig_url: http://saxuality.blogspot.com/2008/05/gwt-netbeans-persistence-and-mysql.html
---
Today I played with GWT and on the server side used JPA (Java Persistence API). As I'm developing my project using Netbeans platform, I just created new GWT project, added persistence and imported entity classes from MySQL database. As a persistence unit I used TopLink.
Now the problem raised when I deployed the project on the server: I got exceptions from TopLink - No persistence provider.
After some googling I finally found answer how to solve this:
copy mysql library (mysql-connector-java-5.1.5-bin.jar
) to your Glassfish domain lib dir (\glassfish-v2ur2\domains\domain1\lib\ext\
) and TopLink should stop complaining.


{% extends "base.html" %} {% block title %}Home{% endblock %} {% block page_content %}
This service provides a way for AGA members to play rated games on 3rd party go servers. The games are ranked separately from in person games and are independent of your AGA rating.
The online-ratings profile is an open protocol -- any 3rd party server can implement it.
Stay tuned for more information...
{% endblock %}


Esi Med is a med application that helps you reach realiable and compitent doctors on a click,
We help you access a doctor from the confort of your house from anywhere in the world
Esi med is realiable, witha 24hours access to a compitent doctor
Contact US:


Nagoya Men's 30 km
Nagoya JPN
Race Winners
(43 runnings)
12 Mar
1995
1:31:56
13 Mar
1994
1:32:07
07 Mar
1993
1:31:25
01 Mar
1992
1:32:59
03 Mar
1991
1:31:19
04 Mar
1990
1:32:15
05 Mar
1989
1:29:52
06 Mar
1988
1:32:02
01 Mar 1987
1:28:52
02 Mar 1986
1:31:40
03 Mar 1985
1:32:09
04 Mar 1984
1:33:51
27 Feb 1983
1:31:28
1:08:58
07 Mar 1982
1:31:45
1:06:52
08 Mar 1981
1:35:16
1:17:50
09 Mar 1980
1:31:04
1:16:10
11 Mar 1979
1:33:11
12 Mar 1978
1:31:58
20 Mar 1977
1:32:09
14 Mar 1976
1:32:30
09 Mar 1975
1:34:16
10 Mar 1974
1:36:04
11 Mar 1973
1:03:00
12 Mar 1972
1:01:59
14 Mar 1971
n/a
15 Mar 1970
n/a
16 Mar 1969
1:01:04
17 Mar 1968
1:02:07
21 Mar 1967
1:02:22
21 Mar 1966
1:01:05.0
21 Mar 1965
---
20 Mar 1964
1:03:08.0
21 Mar 1963
1:01:56
21 Mar 1962
1:00:55
21 Mar 1961
1:03:54
20 Mar 1960
1:06:27
21 Mar 1959
n/a
21 Mar 1958
---
21 Mar 1957
1:06:15
Notes: The inaugural year was 1953. This race was held in association with the Chunichi Marathon thru 1981.
In 1969, the race was moved to Toyohashi, about 60 km southeast of Nagoya. In 1975, this race was renamed the Chunichi
Speed Marathon. In 1982, the race was moved back to Nagoya (the marathon remained in Toyohashi). For the 1987 race,
the name was changed to the Nagoya Men's 30 km. The race distance (for men) was 20 km thru 1973, the race distance
for women was 20 km. The women's 20 km became the Nagoya
Women's Marathon in 1984. The 20 km course in 1982 was most likely
short.
Shin Nakashima (JPN)
Makoto Sasaki (JPN)
Akira Sakemi (JPN)
Koji Shimizu (JPN)
Naoto Suzuki (JPN)
Masanobu Kobo (JPN)
Takeyuki Nakayama-2
Kiyoji Hayashi (JPN)
Toshihiko Seko (JPN)
Yutaka Kanai (JPN)
Takeyuki Nakayama (JPN)
Yasushi Sakaguchi (JPN)
Takao Nakamura (JPN)
Ellen Hart (USA)
Arthur Boileau (CAN)
Cathie Twomey (USA)
Wataru Sakamoto (JPN)
Mie Tanaka (JPN)
Koshiro Kawaguchi (JPN)
Nanae Sasaki (JPN)
Hatsuo Okubo-2
Hatsuo Okubo (JPN)
Yasunori Watanabe (JPN)
Shigeru So (JPN)
Makoto Hattori (JPN)
Akio Usami (JPN)
Naoyuki Sato (JPN)
Nobuyoshi Miura-2
Nobuyoshi Miura (JPN)
Yoshio Yukishima-2
Hayami Tanimura (JPN)
Yoshio Yukishima (JPN)
Tsugumichi Suzuki (JPN)
Teruo Funai-2
n/a
Tetsuya Koizumi (JPN)
Teruo Funai (JPN)
Takayuki Nakao (JPN)
Toyozo Uchino (JPN)
nfn Shiozuka-2
nfn Shiozuka (JPN)
n/a
Fumio Ito (JPN)
Most Wins (1957/1995)
Women
2
nfn Shiozuka (JPN)
1959-60
.
Teruo Funai (JPN)
1963,1966
.
Yoshio Yukishima (JPN)
1968,1970
.
Nobuyoshi Miura (JPN)
1971-72
.
Hatsuo Okubo (JPN)
1978-79
.
Takeyuki Nakayama (JPN)
1985,1989
Prize Money
Prize money cannot be awarded in Japanese road races due to legal restrictions.
Race Time Bias
(sec/km)
Year Men Women 1983 -2.0 -4.9
Competitive Level
(1990-date)
Insufficient data to calculate the competitive level.
Quality Performances
Men (30 km) <1:29 1 <1:30 2 <1:31 2 <1:32 10 <1:33 38 <1:34 85 <1:35 133 <1:36 199 Men (20 km) <1:01:00 1 <1:01:30 3 <1:02:00 5 <1:02:30 7 Women (20 km) <1:09:00 1 <1:10:00 4 <1:11:00 4 <1:12:00 7 <1:13:00 14
Note: 1982 is excluded.
Last Updated on 17 Jan 2006
Contributor: Shigenobu Ota (50)


{% extends 'panel/layout-blank.html' %} {% load core_tags i18n %} {% block title %}{% trans '500 Unexpected Error' %}{% endblock %} {% block content %}
{% blocktrans %}We sorry, an error ocurred. But don't worry. This error will be reported to our developers so it won't happen again.{% endblocktrans %}
{% trans "Go home" %}


Information Communication Technology is so essential for business operations. A right infrastructure system will help the organization in increasing operation effectiveness, lowering operation cost, being more flexible to change.
Our personnel is experienced with extensive knowledge, which allows M supporting client’s from understanding client’s need, designing a right ICT system, building the infrastructure and providing support and maintenance to our customers.
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We are capable of delivering projects in any scale, from a retail shop to a university hostel. Check with us and see how M can assist you in a professional and cost-saving fashion.
Echo sound always an issue to VC. Cost-performing acoustic panels will help to improve the sound interior a lot. Our Capability of designing, shaping and cutting the panels can help enriching the interiors aesthetically.


{% load staticfiles %} {% include "includes/global_styles.html" %}
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Man robbed at knifepoint at Belfast railway station
A MAN was robbed at knifepoint at a railway station in Belfast.
The victim was approached by an unknown man at Yorkgate station who wielded a small flick-type knife and demanded money.
A struggle followed and the assailant made off with the man's mobile phone and suitcase.
The suitcase was found just a few streets away. It appeared an attempt was made to set it on fire, but the contents were undamaged.
The victim sustained some small cuts to his hand in his attempt to hold onto his mobile, but he did not need medical attention.
The robbery happened on Monday night and was reported to police just after 11pm.
Inspector Paul Noble said the victim had "understandably been left shaken by a terrifying ordeal, which could have had grave consequences".
The suspect was described as wearing a grey, hooded top.
Police urged anyone with information or who noticed any suspicious activity to phone them on 101, quoting reference number 1995 of 11/11/19, or Crimestoppers anonymously on 0800 555 111.


The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:
import numpy as np
import pandas as pd
import json
import matplotlib.pyplot as plt
%matplotlib inline
For this assignment, we will use the same subset of the Amazon product review dataset that we used in Module 3 assignment. The subset was chosen to contain similar numbers of positive and negative reviews, as the original dataset consisted of mostly positive reviews.
products = pd.read_csv('amazon_baby_subset.csv')
with open('important_words.json') as important_words_file:
important_words = json.load(important_words_file)
print important_words[:3]
Just like we did previously, we will work with a hand-curated list of important words extracted from the review data. We will also perform 2 simple data transformations:
Refer to Module 3 assignment for more details.
products = products.fillna({'review':''}) # fill in N/A's in the review column
def remove_punctuation(text):
import string
return text.translate(None, string.punctuation)
products['review_clean'] = products['review'].apply(remove_punctuation)
products.head(3)
for word in important_words:
products[word] = products['review_clean'].apply(lambda s : s.split().count(word))
The SFrame products now contains one column for each of the 193 important_words.
products['perfect'][:3]
products.head(2)
We will now split the data into a 90-10 split where 90% is in the training set and 10% is in the validation set. We use seed=1
so that everyone gets the same result.
with open('module-10-assignment-train-idx.json') as train_data_file:
train_data_idx = json.load(train_data_file)
with open('module-10-assignment-validation-idx.json') as validation_data_file:
validation_data_idx = json.load(validation_data_file)
print train_data_idx[:3]
print validation_data_idx[:3]
train_data = products.iloc[train_data_idx]
train_data.head(2)
validation_data = products.iloc[validation_data_idx]
validation_data.head(2)
print 'Training set : %d data points' % len(train_data)
print 'Validation set: %d data points' % len(validation_data)
Just like in the earlier assignments, we provide you with a function that extracts columns from an SFrame and converts them into a NumPy array. Two arrays are returned: one representing features and another representing class labels.
Note: The feature matrix includes an additional column 'intercept' filled with 1's to take account of the intercept term.
def get_numpy_data(dataframe, features, label):
dataframe['constant'] = 1
features = ['constant'] + features
features_frame = dataframe[features]
feature_matrix = features_frame.as_matrix()
label_sarray = dataframe[label]
label_array = label_sarray.as_matrix()
return(feature_matrix, label_array)
feature_matrix_train, sentiment_train = get_numpy_data(train_data, important_words, 'sentiment')
feature_matrix_valid, sentiment_valid = get_numpy_data(validation_data, important_words, 'sentiment')
print feature_matrix_train.shape
print feature_matrix_valid.shape
Note that we convert both the training and validation sets into NumPy arrays.
Warning: This may take a few minutes.
Are you running this notebook on an Amazon EC2 t2.micro instance? (If you are using your own machine, please skip this section)
It has been reported that t2.micro instances do not provide sufficient power to complete the conversion in acceptable amount of time. For interest of time, please refrain from running get_numpy_data
function. Instead, download the binary file containing the four NumPy arrays you'll need for the assignment. To load the arrays, run the following commands:
arrays = np.load('module-10-assignment-numpy-arrays.npz')
feature_matrix_train, sentiment_train = arrays['feature_matrix_train'], arrays['sentiment_train']
feature_matrix_valid, sentiment_valid = arrays['feature_matrix_valid'], arrays['sentiment_valid']
Quiz Question: In Module 3 assignment, there were 194 features (an intercept + one feature for each of the 193 important words). In this assignment, we will use stochastic gradient ascent to train the classifier using logistic regression. How does the changing the solver to stochastic gradient ascent affect the number of features?
Let us now build on Module 3 assignment. Recall from lecture that the link function for logistic regression can be defined as:
$$ P(y_i = +1 | \mathbf{x}_i,\mathbf{w}) = \frac{1}{1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))}, $$where the feature vector $h(\mathbf{x}_i)$ is given by the word counts of important_words in the review $\mathbf{x}_i$.
We will use the same code as in Module 3 assignment to make probability predictions, since this part is not affected by using stochastic gradient ascent as a solver. Only the way in which the coefficients are learned is affected by using stochastic gradient ascent as a solver.
'''
feature_matrix: N * D(intercept term included)
coefficients: D * 1
predictions: N * 1
produces probablistic estimate for P(y_i = +1 | x_i, w).
estimate ranges between 0 and 1.
'''
def predict_probability(feature_matrix, coefficients):
# Take dot product of feature_matrix and coefficients
# YOUR CODE HERE
score = np.dot(feature_matrix, coefficients) # N * 1
# Compute P(y_i = +1 | x_i, w) using the link function
# YOUR CODE HERE
predictions = 1.0/(1+np.exp(-score))
# return predictions
return predictions
Let us now work on making minor changes to how the derivative computation is performed for logistic regression.
Recall from the lectures and Module 3 assignment that for logistic regression, the derivative of log likelihood with respect to a single coefficient is as follows:
$$ \frac{\partial\ell}{\partial w_j} = \sum_{i=1}^N h_j(\mathbf{x}_i)\left(\mathbf{1}[y_i = +1] - P(y_i = +1 | \mathbf{x}_i, \mathbf{w})\right) $$In Module 3 assignment, we wrote a function to compute the derivative of log likelihood with respect to a single coefficient $w_j$. The function accepts the following two parameters:
errors
vector containing $(\mathbf{1}[y_i = +1] - P(y_i = +1 | \mathbf{x}_i, \mathbf{w}))$ for all $i$feature
vector containing $h_j(\mathbf{x}_i)$ for all $i$Complete the following code block:
"""
errors: N * 1
feature: N * 1
derivative: 1
"""
def feature_derivative(errors, feature):
# Compute the dot product of errors and feature
derivative = np.dot(np.transpose(errors), feature)
# Return the derivative
return derivative
Note. We are not using regularization in this assignment, but, as discussed in the optional video, stochastic gradient can also be used for regularized logistic regression.
To verify the correctness of the gradient computation, we provide a function for computing average log likelihood (which we recall from the last assignment was a topic detailed in an advanced optional video, and used here for its numerical stability).
To track the performance of stochastic gradient ascent, we provide a function for computing average log likelihood.
$$\ell\ell_A(\mathbf{w}) = \color{red}{\frac{1}{N}} \sum_{i=1}^N \Big( (\mathbf{1}[y_i = +1] - 1)\mathbf{w}^T h(\mathbf{x}_i) - \ln\left(1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))\right) \Big) $$Note that we made one tiny modification to the log likelihood function (called compute_log_likelihood) in our earlier assignments. We added a $\color{red}{1/N}$ term which averages the log likelihood accross all data points. The $\color{red}{1/N}$ term makes it easier for us to compare stochastic gradient ascent with batch gradient ascent. We will use this function to generate plots that are similar to those you saw in the lecture.
def compute_avg_log_likelihood(feature_matrix, sentiment, coefficients):
indicator = (sentiment==+1)
scores = np.dot(feature_matrix, coefficients)
logexp = np.log(1. + np.exp(-scores))
# scores.shape (53072L, 1L)
# indicator.shape (53072L,)
# Simple check to prevent overflow
mask = np.isinf(logexp)
logexp[mask] = -scores[mask]
lp = np.sum( ( indicator.reshape(scores.shape)-1 )*scores - logexp )/len(feature_matrix)
return lp
Quiz Question: Recall from the lecture and the earlier assignment, the log likelihood (without the averaging term) is given by
$$\ell\ell(\mathbf{w}) = \sum_{i=1}^N \Big( (\mathbf{1}[y_i = +1] - 1)\mathbf{w}^T h(\mathbf{x}_i) - \ln\left(1 + \exp(-\mathbf{w}^T h(\mathbf{x}_i))\right) \Big) $$How are the functions $\ell\ell(\mathbf{w})$ and $\ell\ell_A(\mathbf{w})$ related?
Recall from the lecture that the gradient for a single data point $\color{red}{\mathbf{x}_i}$ can be computed using the following formula:
$$ \frac{\partial\ell_{\color{red}{i}}(\mathbf{w})}{\partial w_j} = h_j(\color{red}{\mathbf{x}_i})\left(\mathbf{1}[y_\color{red}{i} = +1] - P(y_\color{red}{i} = +1 | \color{red}{\mathbf{x}_i}, \mathbf{w})\right) $$Computing the gradient for a single data point
Do we really need to re-write all our code to modify $\partial\ell(\mathbf{w})/\partial w_j$ to $\partial\ell_{\color{red}{i}}(\mathbf{w})/{\partial w_j}$?
Thankfully No!. Using NumPy, we access $\mathbf{x}_i$ in the training data using feature_matrix_train[i:i+1,:]
and $y_i$ in the training data using sentiment_train[i:i+1]
. We can compute $\partial\ell_{\color{red}{i}}(\mathbf{w})/\partial w_j$ by re-using all the code written in feature_derivative and predict_probability.
We compute $\partial\ell_{\color{red}{i}}(\mathbf{w})/\partial w_j$ using the following steps:
feature_matrix_train[i:i+1,:]
as the first parameter.sentiment_train[i:i+1]
.feature_matrix_train[i:i+1, j]
as one of the parameters. Let us follow these steps for j = 1
and i = 10
:
j = 1 # Feature number
i = 10 # Data point number
coefficients = np.zeros(194) # A point w at which we are computing the gradient.
predictions = predict_probability(feature_matrix_train[i:i+1,:], coefficients)
indicator = (sentiment_train[i:i+1]==+1)
errors = indicator - predictions
gradient_single_data_point = feature_derivative(errors, feature_matrix_train[i:i+1,j])
print "Gradient single data point: %s" % gradient_single_data_point
print " --> Should print 0.0"
Quiz Question: The code block above computed $\partial\ell_{\color{red}{i}}(\mathbf{w})/{\partial w_j}$ for j = 1
and i = 10
. Is $\partial\ell_{\color{red}{i}}(\mathbf{w})/{\partial w_j}$ a scalar or a 194-dimensional vector?
scalar
Stochastic gradient estimates the ascent direction using 1 data point, while gradient uses $N$ data points to decide how to update the the parameters. In an optional video, we discussed the details of a simple change that allows us to use a mini-batch of $B \leq N$ data points to estimate the ascent direction. This simple approach is faster than regular gradient but less noisy than stochastic gradient that uses only 1 data point. Although we encorage you to watch the optional video on the topic to better understand why mini-batches help stochastic gradient, in this assignment, we will simply use this technique, since the approach is very simple and will improve your results.
Given a mini-batch (or a set of data points) $\mathbf{x}_{i}, \mathbf{x}_{i+1} \ldots \mathbf{x}_{i+B}$, the gradient function for this mini-batch of data points is given by: $$ \color{red}{\sum_{s = i}^{i+B}} \frac{\partial\ell_{s}}{\partial w_j} = \color{red}{\sum_{s = i}^{i + B}} h_j(\mathbf{x}_s)\left(\mathbf{1}[y_s = +1] - P(y_s = +1 | \mathbf{x}_s, \mathbf{w})\right) $$
Computing the gradient for a "mini-batch" of data points
Using NumPy, we access the points $\mathbf{x}_i, \mathbf{x}_{i+1} \ldots \mathbf{x}_{i+B}$ in the training data using feature_matrix_train[i:i+B,:]
and $y_i$ in the training data using sentiment_train[i:i+B]
.
We can compute $\color{red}{\sum_{s = i}^{i+B}} \partial\ell_{s}/\partial w_j$ easily as follows:
j = 1 # Feature number
i = 10 # Data point start
B = 10 # Mini-batch size
coefficients = np.zeros(194) # A point w at which we are computing the gradient.
predictions = predict_probability(feature_matrix_train[i:i+B,:], coefficients)
indicator = (sentiment_train[i:i+B]==+1)
errors = indicator - predictions
gradient_mini_batch = feature_derivative(errors, feature_matrix_train[i:i+B,j])
print "Gradient mini-batch data points: %s" % gradient_mini_batch
print " --> Should print 1.0"
Quiz Question: The code block above computed
$\color{red}{\sum_{s = i}^{i+B}}\partial\ell_{s}(\mathbf{w})/{\partial w_j}$
for j = 10
, i = 10
, and B = 10
. Is this a scalar or a 194-dimensional vector?
Quiz Question: For what value of B
is the term
$\color{red}{\sum_{s = 1}^{B}}\partial\ell_{s}(\mathbf{w})/\partial w_j$
the same as the full gradient
$\partial\ell(\mathbf{w})/{\partial w_j}$? Hint: consider the training set we are using now.
scalar
(47780L, 194L)
It is a common practice to normalize the gradient update rule by the batch size B:
$$ \frac{\partial\ell_{\color{red}{A}}(\mathbf{w})}{\partial w_j} \approx \color{red}{\frac{1}{B}} {\sum_{s = i}^{i + B}} h_j(\mathbf{x}_s)\left(\mathbf{1}[y_s = +1] - P(y_s = +1 | \mathbf{x}_s, \mathbf{w})\right) $$In other words, we update the coefficients using the average gradient over data points (instead of using a summation). By using the average gradient, we ensure that the magnitude of the gradient is approximately the same for all batch sizes. This way, we can more easily compare various batch sizes of stochastic gradient ascent (including a batch size of all the data points), and study the effect of batch size on the algorithm as well as the choice of step size.
Now we are ready to implement our own logistic regression with stochastic gradient ascent. Complete the following function to fit a logistic regression model using gradient ascent:
from math import sqrt
def logistic_regression_SG(feature_matrix, sentiment, initial_coefficients, step_size, batch_size, max_iter):
log_likelihood_all = []
# make sure it's a numpy array
coefficients = np.array(initial_coefficients)
# set seed=1 to produce consistent results
np.random.seed(seed=1)
# Shuffle the data before starting
permutation = np.random.permutation(len(feature_matrix))
feature_matrix = feature_matrix[permutation,:]
sentiment = sentiment[permutation]
i = 0 # index of current batch
# Do a linear scan over data
for itr in xrange(max_iter):
# Predict P(y_i = +1|x_i,w) using your predict_probability() function
# Make sure to slice the i-th row of feature_matrix with [i:i+batch_size,:]
### YOUR CODE HERE
predictions = predict_probability(feature_matrix[i:(i+batch_size),:], coefficients)
# Compute indicator value for (y_i = +1)
# Make sure to slice the i-th entry with [i:i+batch_size]
### YOUR CODE HERE
indicator = (sentiment[i:i+batch_size]==+1)
# Compute the errors as indicator - predictions
errors = indicator - predictions
for j in xrange(len(coefficients)): # loop over each coefficient
# Recall that feature_matrix[:,j] is the feature column associated with coefficients[j]
# Compute the derivative for coefficients[j] and save it to derivative.
# Make sure to slice the i-th row of feature_matrix with [i:i+batch_size,j]
### YOUR CODE HERE
derivative = feature_derivative(errors, feature_matrix[i:i+batch_size,j])
# Compute the product of the step size, the derivative, and
# the **normalization constant** (1./batch_size)
### YOUR CODE HERE
coefficients[j] += step_size * derivative * 1. / batch_size
# Checking whether log likelihood is increasing
# Print the log likelihood over the *current batch*
lp = compute_avg_log_likelihood(feature_matrix[i:i+batch_size,:], sentiment[i:i+batch_size],
coefficients)
log_likelihood_all.append(lp)
if itr <= 15 or (itr <= 1000 and itr % 100 == 0) or (itr <= 10000 and itr % 1000 == 0) \
or itr % 10000 == 0 or itr == max_iter-1:
data_size = len(feature_matrix)
print 'Iteration %*d: Average log likelihood (of data points [%0*d:%0*d]) = %.8f' % \
(int(np.ceil(np.log10(max_iter))), itr, \
int(np.ceil(np.log10(data_size))), i, \
int(np.ceil(np.log10(data_size))), i+batch_size, lp)
# if we made a complete pass over data, shuffle and restart
i += batch_size
if i+batch_size > len(feature_matrix):
permutation = np.random.permutation(len(feature_matrix))
feature_matrix = feature_matrix[permutation,:]
sentiment = sentiment[permutation]
i = 0
# We return the list of log likelihoods for plotting purposes.
return coefficients, log_likelihood_all
Note. In practice, the final set of coefficients is rarely used; it is better to use the average of the last K sets of coefficients instead, where K should be adjusted depending on how fast the log likelihood oscillates around the optimum.
The following cell tests your stochastic gradient ascent function using a toy dataset consisting of two data points. If the test does not pass, make sure you are normalizing the gradient update rule correctly.
sample_feature_matrix = np.array([[1.,2.,-1.], [1.,0.,1.]])
sample_sentiment = np.array([+1, -1])
coefficients, log_likelihood = logistic_regression_SG(sample_feature_matrix, sample_sentiment, np.zeros(3), step_size=1., batch_size=2, max_iter=2)
print '-------------------------------------------------------------------------------------'
print 'Coefficients learned :', coefficients
print 'Average log likelihood per-iteration :', log_likelihood
if np.allclose(coefficients, np.array([-0.09755757, 0.68242552, -0.7799831]), atol=1e-3)\
and np.allclose(log_likelihood, np.array([-0.33774513108142956, -0.2345530939410341])):
# pass if elements match within 1e-3
print '-------------------------------------------------------------------------------------'
print 'Test passed!'
else:
print '-------------------------------------------------------------------------------------'
print 'Test failed'
For the remainder of the assignment, we will compare stochastic gradient ascent against batch gradient ascent. For this, we need a reference implementation of batch gradient ascent. But do we need to implement this from scratch?
Quiz Question: For what value of batch size B
above is the stochastic gradient ascent function logistic_regression_SG act as a standard gradient ascent algorithm? Hint: consider the training set we are using now.
Instead of implementing batch gradient ascent separately, we save time by re-using the stochastic gradient ascent function we just wrote — to perform gradient ascent, it suffices to set batch_size
to the number of data points in the training data. Yes, we did answer above the quiz question for you, but that is an important point to remember in the future :)
Small Caveat. The batch gradient ascent implementation here is slightly different than the one in the earlier assignments, as we now normalize the gradient update rule.
We now run stochastic gradient ascent over the feature_matrix_train for 10 iterations using:
initial_coefficients = np.zeros(194)
step_size = 5e-1
batch_size = 1
max_iter = 10
coefficients, log_likelihood = logistic_regression_SG(feature_matrix_train, sentiment_train,\
initial_coefficients=np.zeros(194),\
step_size=5e-1, batch_size=1, max_iter=10)
plt.plot(log_likelihood)
plt.show()
Quiz Question. When you set batch_size = 1
, as each iteration passes, how does the average log likelihood in the batch change?
 When you set batch_size = len(feature_matrix_train)
, as each iteration passes, how does the average log likelihood in the batch change?
To make a fair comparison betweeen stochastic gradient ascent and batch gradient ascent, we measure the average log likelihood as a function of the number of passes (defined as follows): $$ [\text{# of passes}] = \frac{[\text{# of data points touched so far}]}{[\text{size of dataset}]} $$
Quiz Question Suppose that we run stochastic gradient ascent with a batch size of 100. How many gradient updates are performed at the end of two passes over a dataset consisting of 50000 data points?
1000
With the terminology in mind, let us run stochastic gradient ascent for 10 passes. We will use
step_size=1e-1
batch_size=100
initial_coefficients
to all zeros.step_size = 1e-1
batch_size = 100
num_passes = 10
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
step_size=1e-1, batch_size=100, max_iter=num_iterations)
plt.plot(log_likelihood_batch)
plt.show()
We provide you with a utility function to plot the average log likelihood as a function of the number of passes.
def make_plot(log_likelihood_all, len_data, batch_size, smoothing_window=1, label=''):
plt.rcParams.update({'figure.figsize': (9,5)})
log_likelihood_all_ma = np.convolve(np.array(log_likelihood_all), \
np.ones((smoothing_window,))/smoothing_window, mode='valid')
plt.plot(np.array(range(smoothing_window-1, len(log_likelihood_all)))*float(batch_size)/len_data,
log_likelihood_all_ma, linewidth=4.0, label=label)
plt.rcParams.update({'font.size': 16})
plt.tight_layout()
plt.xlabel('# of passes over data')
plt.ylabel('Average log likelihood per data point')
plt.legend(loc='lower right', prop={'size':14})
make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,
label='stochastic gradient, step_size=1e-1')
The plotted line oscillates so much that it is hard to see whether the log likelihood is improving. In our plot, we apply a simple smoothing operation using the parameter smoothing_window
. The smoothing is simply a moving average of log likelihood over the last smoothing_window
"iterations" of stochastic gradient ascent.
make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,
smoothing_window=30, label='stochastic gradient, step_size=1e-1')
Checkpoint: The above plot should look smoother than the previous plot. Play around with smoothing_window
. As you increase it, you should see a smoother plot.
To compare convergence rates for stochastic gradient ascent with batch gradient ascent, we call make_plot()
multiple times in the same cell.
We are comparing:
step_size = 0.1
, batch_size=100
step_size = 0.5
, batch_size=len(feature_matrix_train)
Write code to run stochastic gradient ascent for 200 passes using:
step_size=1e-1
batch_size=100
initial_coefficients
to all zeros.step_size = 1e-1
batch_size = 100
num_passes = 200
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
## YOUR CODE HERE
coefficients_sgd, log_likelihood_sgd = logistic_regression_SG(feature_matrix_train, sentiment_train,
initial_coefficients=np.zeros(194),
step_size=1e-1, batch_size=100, max_iter=num_iterations)
# YOUR CODE HERE
coefficients_batch, log_likelihood_batch = logistic_regression_SG(feature_matrix_train, sentiment_train,\
initial_coefficients=np.zeros(194),\
step_size=5e-1, batch_size=len(feature_matrix_train), max_iter=200)
We compare the convergence of stochastic gradient ascent and batch gradient ascent in the following cell. Note that we apply smoothing with smoothing_window=30
.
make_plot(log_likelihood_sgd, len_data=len(feature_matrix_train), batch_size=100,
smoothing_window=30, label='stochastic, step_size=1e-1')
make_plot(log_likelihood_batch, len_data=len(feature_matrix_train), batch_size=len(feature_matrix_train),
smoothing_window=1, label='batch, step_size=5e-1')
Quiz Question: In the figure above, how many passes does batch gradient ascent need to achieve a similar log likelihood as stochastic gradient ascent?
In previous sections, we chose step sizes for you. In practice, it helps to know how to choose good step sizes yourself.
To start, we explore a wide range of step sizes that are equally spaced in the log space. Run stochastic gradient ascent with step_size
set to 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, and 1e2. Use the following set of parameters:
initial_coefficients=np.zeros(194)
batch_size=100
max_iter
initialized so as to run 10 passes over the data.batch_size = 100
num_passes = 10
num_iterations = num_passes * int(len(feature_matrix_train)/batch_size)
coefficients_sgd = {}
log_likelihood_sgd = {}
for step_size in np.logspace(-4, 2, num=7):
coefficients_sgd[step_size], log_likelihood_sgd[step_size] = logistic_regression_SG(feature_matrix_train, sentiment_train,\
initial_coefficients=np.zeros(194),\
step_size=step_size, batch_size=batch_size, max_iter=num_iterations)
Now, we will plot the change in log likelihood using the make_plot
for each of the following values of step_size
:
step_size = 1e-4
step_size = 1e-3
step_size = 1e-2
step_size = 1e-1
step_size = 1e0
step_size = 1e1
step_size = 1e2
For consistency, we again apply smoothing_window=30
.
for step_size in np.logspace(-4, 2, num=7):
make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,
smoothing_window=30, label='step_size=%.1e'%step_size)
Now, let us remove the step size step_size = 1e2
and plot the rest of the curves.
for step_size in np.logspace(-4, 2, num=7)[0:6]:
make_plot(log_likelihood_sgd[step_size], len_data=len(train_data), batch_size=100,
smoothing_window=30, label='step_size=%.1e'%step_size)
Quiz Question: Which of the following is the worst step size? Pick the step size that results in the lowest log likelihood in the end.
Quiz Question: Which of the following is the best step size? Pick the step size that results in the highest log likelihood in the end.


Arrow keys to move, space to shoot
Serious business
Points:
Note
: Jump on boxes to get points, shoot them to get extra points. Move to right side to level up. Don't fall.


|
cds
2.3.1
|
Array of locks. More...
#include <cds/sync/lock_array.h>
Public Types | |
| typedef Lock | lock_type |
| lock type | |
| typedef SelectPolicy | select_cell_policy |
| Cell selection policy functor. | |
Public Member Functions | |
| lock_array (size_t nCapacity) | |
| Constructs array of locks. More... | |
| lock_array (size_t nCapacity, select_cell_policy const &policy) | |
| Constructs array of lock and copy cell selection policy. More... | |
| lock_array (size_t nCapacity, select_cell_policy &&policy) | |
| Constructs array of lock and move cell selection policy. More... | |
| ~lock_array () | |
| Destructs array of locks and frees used memory. | |
| template<typename Q > | |
| size_t | lock (Q const &hint) |
Locks a lock at cell hint . More... | |
| template<typename Q > | |
| size_t | try_lock (Q const &hint) |
Try lock a lock at cell hint . More... | |
| void | unlock (size_t nCell) |
Unlock the lock specified by index nCell . | |
| void | lock_all () |
| Lock all. | |
| void | unlock_all () |
| Unlock all. | |
| lock_type & | at (size_t nCell) const |
Get lock at cell nCell . More... | |
| size_t | size () const |
| Size of lock array. | |
Static Public Attributes | |
| static size_t const | c_nUnspecifiedCell = (size_t) -1 |
| failed try_lock call result | |
Protected Attributes | |
| lock_type * | m_arrLocks |
| lock array | |
| size_t const | m_nCapacity |
| array capacity | |
| select_cell_policy | m_SelectCellPolicy |
| Cell selection policy. | |
Array of locks.
The lock array is useful for building fine-grained lock-based data structure based on striping technique. Instead of locking access to data struct (a hash map, for example) at whole, the striping locks only part of the map (a bucket). So, access to different buckets can be simultaneous.
Template arguments:
Lock
- lock type, for example, std::mutex
, cds::sync::spin_lock
SelectPolicy
- array cell selection policy, the default is mod_select_policy Available policies: trivial_select_policy, pow2_select_policy, mod_select_policy.Alloc
- memory allocator for arrayTo determine array's cell the selection policy SelectPolicy
functor is used. Two arguments are passed to the policy:
nHint
- a hint to calculate cell index in the lock array. Usually, it is a hash value.nCapacity
- the size of the lock array The functor should return the index in the lock array.Note that the type of nHint
parameter can be any.
|
inline |
Constructs array of locks.
Allocates the array and initializes all locks as unlocked.
| [in] | nCapacity | Array size |
|
inline |
Constructs array of lock and copy cell selection policy.
Allocates the array and initializes all locks as unlocked.
| [in] | nCapacity | Array size |
| policy | Cell selection policy (copy-constructible) |
|
inline |
Constructs array of lock and move cell selection policy.
Allocates the array and initializes all locks as unlocked.
| [in] | nCapacity | Array size |
| policy | Cell selection policy (move-constructible) |
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inline |
Get lock at cell nCell
.
Precondition: nCell < size()
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inline |
Locks a lock at cell hint
.
To define real array's cell which should be locked, select_cell_policy is used. The target cell is a result of select_cell_policy( hint, size())
.
Returns the index of locked lock.
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inline |
Try lock a lock at cell hint
.
To define real array's cell which should be locked, select_cell_policy is used. The target cell is a result of select_cell_policy( hint, size())
.
Returns the index of locked lock if success, c_nUnspecifiedCell constant otherwise.