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---
layout: post
title: Stephen Colbert and the National Portrait Gallery
date: '2008-01-19T19:04:00.000-05:00'
author: Pat Hyatt
description: I visited Mr. Colbert at the National Portrait Gallery.
tags:
- Colbert
- Stephen Colbert
- The Colbert Report
- National
- National Portrait Gallery
modified_time: '2008-01-19T19:27:51.518-05:00'
thumbnail: http://4.bp.blogspot.com/_K0T9JgGHK5g/R5KSX4gMadI/AAAAAAAAABc/WJ8Wis0FvCY/s72-c/colbert.JPG
---
Stephen Colbert from The Colbert Report recently was granted a limited showing in the
National Portrait Gallery in D.C. this week with his portrait within portraits rendering. The question will his
display be appreciated?
Well the answer is an obvious yes. Not only was the Colbert display crowded with visitors, but there was a
line formed just to take pictures. On top of that, there were 2 security guards to keep the walkways clear and lots of
giddiness. By far the most crowded display I've seen in a museum here. Just amazing. I highly recommend visiting the
display if you have the ability. Its filled with young enthusiasm, laughter and a lack of stiffness you normally get
when in a museum.


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The Lorax: Which way does a tree fall?
The Once-ler: Uh, down?
The Lorax: A tree falls the way it leans. Be careful which way you lean.
- Dr. Seuss, The Lorax
All four individuals made interesting early contributions to a mathematical structure that we now know as a tree. In this blog post, we will briefly explore their unique contributions and see how the tree structure was introduced in both mathematics and computer science.
If you find any inaccuracies in this blog post, please do let me know.
Before we dive into Kirchhoff’s contribution, let us review what a free tree and a fundamental cycle are.
A free tree is a connected graph with no designated root and no cycles, hence its alternate name, an unrooted tree. This distinguishes it from an arborescence, which consists of a designated root node.
Although there are many intuitive definitions of a fundamental cycle, one of the simplest comes from Casteels on Math Exchange:
Let \(G\) be a connected graph, and let \(T\) be a spanning tree of \(G\). Let \(e\) be an edge in \(G\) between vertices \(v\) and \(w\) that is not in \(T\). Now in \(T\) there is a unique path between \(v\) and \(w\), and since \(e\) is not in \(T\), that path does not use \(e\). Therefore, that path together with \(e\) forms a cycle in \(G\). A cycle built this way is called a fundamental cycle.
Here is a neat visualization of the same:
So, what is the relation between free trees, fundamental cycles, and Gustav Kirchhoff? Well, as mentioned in Donald Knuth’s TAOCP Volume 1, Gustav Kirchhoff was one of the first to use free trees to find a set of fundamental cycles in an electrical network. His 1847 paper also formed the basis for the now ubiquitous Kirchhoff’s current and voltage laws.
Kirchhoff used \(m\) to denote the number of junctions (nodes) and \(n\) to denote the number of branches (edges). While stating Theorem 2, he mentioned the equation \(\mu = n - m + 1\), which finds the number of fundamental cycles in a graph \( \mu \). Furthermore, the number of equations needed for Kirchhoff’s Voltage law (Theorem 1 in the paper) is given by the least number of edges that must be removed from the graph for all the cycles to be destroyed.
Interestingly, Donald Knuth also cites Karl G. C. von Staudt’s 1847 work Geometrie der Lage (pages 20 and 21) as an early contribution to the field of trees. In his work, von Staudt showed proof of Euler's polyhedron formula, \( n - e + f =2 \), where \(n\) is the number of vertices, \(e\) is the number of edges, and \(f\) is the number of faces. Euler mentioned his result in a letter to Christian Goldbach in 1750; however, he did not give the first correct proof of his formula. The French mathematician Adrian Marie Legendre is often cited as having given the first correct proof of this formula, somewhere in 1794.
So, what was von Staudt’s contribution? As it turns out, von Staudt presented proof of Euler's polyhedron formula that relied on the use of trees. Although the original text is in German, Robin Whitty has neatly summarized the proof in one of his "Theorem of the Day" posts:
For graph \(G\), take the dual graph \(G*\) whose vertices are the plane faces and whose edges join dual vertices by crossing edges of \(G\). Take a spanning tree \(T\) in \(G\): it has \(n\) vertices and \(n - 1\) edges. The remaining edges of \(G\) are crossed by a spanning tree \(T*\) of \(G*\): it has \(f\) vertices and \(f - 1\) edges. Every edge of \(G\) is in \(T\) or is crossed by an edge of \(T*\). So \(e = (n - 1) + (f - 1)\).
A visualization of this proof is shown in Figure 2.
It was not until ten years later, in 1857, that Arthur Cayley formulated the term "tree" in his paper "On the theory of the analytical forms called trees." Over three decades (1857-89), he published a series of papers on the theory of trees and their applications. As stated by Donald Knuth, Cayley seemed to be unaware of the previous work of Kirchhoff and von Staudt. His investigations mainly involved the study of isomers in chemistry and the structure of algebraic formulas.
Cayley’s 1857 contribution was using trees as an organizational tool for differentiation. This is succinctly expressed by Prof. Gerald M. Lodder at NMSU:
In an 1857 publication [9], Cayley introduces the term “tree” to describe the logical branching that occurs when iterating the fundamental procedure of differentiation. Calculus is not the main concern here, but instead an organizational tool is developed that provides a visual overview of the individual terms under differentiation. This tool is used today in quite different situations from networks in computer science to finding efficient delivery routes in the transportation industry. To introduce Cayley’s paper, let \(\partial_x\) denote differentiation with respect to \(x\) and let \(\partial_y\) denote differentiation with respect to \(y\). Then \(\partial_x(x^2 y) = 2xy\) and \(\partial_y(x^2y) = x^2\), given that \(x\) and \(y\) are independent variables. The symbols \(\partial_x\) and \(\partial_y\) are called operators, while the expression \(x^2 y\) itself is an operand. Note that the symbols \(\partial_x\) and \(\partial_y\) are applied to (operate on) functions written to the right of the symbol. If the function is written to the left, such as \(x^2 y \partial_x\), Cayley dubs the entire expression an operandator, and \(x^2 y\) remains unaltered by the operator on the right. Cayley wishes to study how operandators interact among themselves. Let \(P = x^2 y \partial_x\) and \(Q = xy \partial_y\). Then PQ is the operandator given by \[PQ = x^2 y \partial_x(xy \partial_y) = x^2 y(y \partial_y) = x^2y^2\partial_y\] and \(QP\) is the operandator given by \[QP = xy \partial_y(x^2y \partial_x) = xy(x^2\partial_x) = x^3y\partial_x\] In the above example, \(QP \ne PQ\). For operandators \(Q\), \(P\), \(U\), what should be the meaning of \(QPU\)? Do the groupings \(Q(PU)\) and \((QP)U\) yield the same result, with \(Q\), \(P\), and \(U\) are in the same relative order?
Here is the relevant part from Cayley’s 1857 paper:
Later, in 1874, Cayley published another paper wherein he used trees to enumerate the isomers of compounds of form \(C_n H_{2n+2}\).
Charles Dodgson, better known by his pen name Lewis Carroll, made a fascinating observation in the theory of trees. In 1972, W. W. Bartley \(III\) published a paper on Carroll's lost book on logic, which contained the Method of Trees, one of the earliest uses of truth trees to prove or refute a logical formula.
Nowadays, truth trees are widely used to prove theorems, analyze the logical structure of mathematical proofs, and formalize the rules of grammar in artificial intelligence and linguistics. While discussing the Method of Trees, W. W. Bartley \(III\) writes the following:
METHOD OF TREES was developed by Dodgson as a means of testing the validity of a conclusion derived from premises. It is strikingly similar to the "trees" frequently used by contemporary logicians. The basic idea is to assume that the premises are true but the conclusion is false (and its denial is true). If combining the denial of the conclusion with the premises leads to an absurdity, it shows that the premises do indeed prove the conclusion. The explanation here has been reset from the section describing "The Method of Trees " in Dodgson's unpublished work. It deals with a simple tree that does not require branching.
Interestingly, in his discussion of the Method of Trees, W. W. Bartley \(III\) notes that Lewis Carroll applied this method to the Crocodile dilemma in his unpublished work.
A curious reader can explore more about Carroll’s approach in Bartley’s paper.
Donald Knuth mentions something fascinating in TAOCP about the history of trees in computer science:
Tree structures represented explicitly in computer memory were originally used for applications to algebraic formula manipulation. The machine language for several early computers used a three-address code to represent the computation of arithmetic expressions; the latter is equivalent to theINFO
,LLINK
, andRLINK
of a binary tree representation. In 1952, H. G. Kahrimanian developed algorithms for differentiating algebraic formulas represented in an extended three-address code; see Symposium on Automatic Programming (Washington, D.C.: Office of Naval Research, May 1954), 6–14.
Since then, tree structures in various guises have been studied independently by many people in connection with numerous computer applications .....
This might make H. G. Kahrimanian's 1952 paper one of the earliest references to a tree-like structure (in guise) in computing. Interestingly, for a long time, I thought that his thesis was lost. Then a few months back, I came across the proceedings of the 1954 Symposium on Automatic Programming on Google Books. And sure enough, inside it was Kahrimanian’s paper Analytical Differentiation by a Digital Computer.
Little is known about Harry G. Kahrimanian beyond the fact that he completed his Master's thesis at Temple University in 1953 and later worked at the Naval Aviation Supply Office in Philadelphia, Pennsylvania. However, Kurt W. Beyer's book Grace Hopper and the Invention of the Information Age contains fascinating information about him:
Hopper had the habit of assigning the most difficult technical problems to the youngest and least experienced members of a team. Mildred Koss, shortly after arriving at the Eckert-Mauchly Computer Corporation, was handed the task of having the UNIVAC automatically edit its data for printing. The result was Koss’s “editing generator,” a sophisticated piece of code that created margins, titles, headings, and page numbers on the fly and represented the first attempt to use a computer for word processing. Harry Kahrimanian, just out of college, got an even more daunting assignment: he was asked to create a program that could automatically solve differential equations. Kahrimanian’s differentiator was a watershed in applied mathematics. The user had merely to enter the equation and indicate the number of n derivatives; the computer did the rest. Kahrimanian’s achievement was even more impressive in light of Hopper’s own long and arduous experience working alongside the famed mathematician John von Neumann to solve differential equations on the Harvard Mark I. Had Kahrimanian’s differentiator been available to von Neumann and Hopper, they could have solved the Manhattan Project’s implosion problem in days instead of months.
To understand Kahrimanian's contribution to trees, it's important to first understand Three-address codes (TAC), which is an intermediate representation of a program that uses at most three operands to represent an operation. TAC can be represented as a tree-like structure where the nodes represent the operands and operators, and the edges represent the flow of data between them. For instance,
For Kahrimanian's differentiator system, he defined a symbolic language to express elementary functions and their derivatives using three-address code expressions like:
These expressions were then used to define a function in three-address code, which in turn could be represented as a tree-like structure.
However, it's worth noting that Kahrimanian did not use the term "tree" in his paper to describe these structures.
Lastly, Donald Knuth had the following to say about the explicit mention of trees in the early days of Computer Science:
Since then, tree structures in various guises have been studied independently by many people in connection with numerous computer applications, but the basic techniques for tree manipulation (not general List manipulation) have seldom appeared in print except in detailed description of particular algorithms. The first general survey was made in connection with a more general study of all data structures by K. E. Iverson and L. R. Johnson [IBM Corp. research reports RC-390, RC-603, 1961; see Iverson, A Programming Language (New York: Wiley, 1962), Chapter 3]. See also G. Salton, CACM 5 (1962), 103–114.
And that’s it, folks! I hope you enjoyed this little adventure I took you on today. We spend most of our lives trying to learn the what and the why of our fields, but it is nice to stop once in a while and ask ourselves: how did we achieve all of this, who was responsible for it, and what problems were they initially trying to solve?
Make no mistake, I am sure that I have missed exploring many contributions. However, I encourage curious readers to further explore the various citations I’ve sprinkled throughout this blog. Who knows? You might find some treasures in the works of Borchardt, Listing, Jordan, and Borůvka.


public class ChangedArtifact
extends java.lang.Object
Commit
. They basically provide two lists of strings, from which one contains all lines
belonging to the diff header (general information about the artifact, the number of changed lines, etc.) and the
other contains the actual contents of the artifact including the changed lines.| Constructor and Description |
|---|
ChangedArtifact()
Constructs a new changed artifact.
|
| Modifier and Type | Method and Description |
|---|---|
void |
addArtifactName(java.lang.String artifactName)
Adds the given string as the name of this changed artifact.
|
void |
addArtifactPath(java.lang.String artifactPath)
Adds the given string as the relative path to this changed artifact in the repository.
|
void |
addContentLine(java.lang.String contentLine)
Adds a new string representing a line of content of this changed artifact.
|
void |
addDiffHeaderLine(java.lang.String diffHeaderLine)
Adds a new string representing a line in the diff header to this changed artifact's diff header.
|
java.lang.String |
getArtifactName()
Returns the string denoting the name of this changed artifact.
|
java.lang.String |
getArtifactPath()
Returns the string denoting the relative path to this changed artifact in the repository.
|
java.util.List<java.lang.String> |
getContent()
Return the content of this changed artifact.
|
java.util.List<java.lang.String> |
getDiffHeader()
Returns the diff header of this changed artifact.
|
java.lang.String |
toString() |
public void addArtifactPath(java.lang.String artifactPath)
artifactPath
- the relative path to this changed artifact in the repository; should never be
null
public void addArtifactName(java.lang.String artifactName)
artifactName
- the name of this changed artifact (the file it represents); should never be null
public void addDiffHeaderLine(java.lang.String diffHeaderLine)
diffHeaderLine
- a single line of the diff header; should never be null
public void addContentLine(java.lang.String contentLine)
contentLine
- a single content line; should never be null
public java.lang.String getArtifactPath()
null
, but may be
empty.public java.lang.String getArtifactName()
null
, but may be
empty.public java.util.List<java.lang.String> getDiffHeader()
null
, but may be emptypublic java.util.List<java.lang.String> getContent()
null
, but
may be emptypublic java.lang.String toString()
toString
in class java.lang.Object


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What are you doing tomorrow?
Lörem ipsum dolör. Sit amet cönsectetur adipisicing elit sed dö eiusmod. Tempör incididunt ut laböre, et dölöre magnå. Aliquå såpien sollicitudin mi. Dapibus blåndit quäm. Erös risus ornåre leo årcu.
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What are you doing tomorrow?
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Cardigan before they sold out chartreuse fashion axe, 90's leggings mixtape stumptown waistcoat quinoa. Whatever bicycle rights pop-up keytar. Keytar fingerstache readymade four loko, fanny pack kickstarter.
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The challenge of learning Swedish
Most Swedes speak English – which certainly helps a first-time visitor in Stockholm. But it makes learning Swedish somewhat harder, according to former expat Charlotte West.
The challenge of learning Swedish
Most Swedes speak English – which certainly helps a first-time visitor in Stockholm. But it makes learning Swedish somewhat harder, according to former expat Charlotte West.
Newly arrived
I arrived in Sweden in August 2002 armed with Prisma’s Abridged English-Swedish and Swedish-English Dictionary and an eight-week intensive Swedish course from the University of California at Berkeley behind me. Four years later, the dog-eared dictionary had grammar notes scribbled in the margins and I had become a fluent Swedish speaker, more or less.
Swedish Word of the Day
A big part of the reason my spoken Swedish got good is that I quickly met Swedish friends who were willing to help me practise. One of our techniques was keeping track of our ‘Swedish Word of the Day’ on a list tacked to the kitchen cupboard.


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1. Numbers divisible by 3 become "ping"
2. Numbers divisible by 5 become "pong"
3. Numbers divisible by 15 become "pingpong"


[gecode-users] question about lexical ordering constraint and others
Christopher Jefferson
caj at cs.st-andrews.ac.uk
Tue May 10 23:02:16 CEST 2011
On 10 May 2011, at 17:35, Kish Shen wrote:
> Hi,
>
> I have a few (more) question about various Gecode constraints that I am interfacing to ECLiPSe:
>
> 1) the constraint for lexical ordering
>
> rel(Home, IntVarArgs, IntRelType, IntVarArgs)
>
> requires the two arrays to be the same size. It seems the same restriction applies in the global constraint catalog for the lexical ordering constraints as well. However, the existing ECLiPSe lexical ordering constraints does not have this restriction -- i.e. the two sequences of numbers being compared can be different lengths. What is the reason for the same length restriction?
>
> The lexical ordering for sequences of different length used in ECLiPSe seems to be the natural one, where missing numbers in the shorter sequence are replaced by 'bottom', i.e. a value smaller than any number.
> So (using . to separate numbers in a sequence)
>
> 1.3 > 1.2.5
>
> 1.2.5 > 1.2
>
> etc.
>
> Could such a definition of ordering be used in the Gecode version as well?
The easiest way to implement this is as follows:
If the second array is longer, just truncate it to the length of the first.
If the first array is longer, truncate it to the length of the first and change the lex<= constraint to a lex< (assuming gecode has a lex< constraint).
If you don't have a lex< constraint, you can make one by sticking 1 on the end of the first array and 0 on the end of the second (once the arrays are the same length).
Chris
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Applications of Distributed Version Control Technology to the Creation of Digital Scholarly Editions
Group for Experimental Methods in the Humanities
What is version control, and why should I care?
As textual scholars, we care about:
- textual difference
- versions of texts
- collaborative editing
- semantic metadata
So do version control systems! It’s a perfect match!
Problems with Electronic Texts
- Difficult to edit
- Editorial histories often obscure
- Stuck in the hands of the few
Version control systems:
- Store every version of a document / project
- Store metadata about every edit: who made the change, when the change happened, what changed
- Facilitate collaboration, analysis, version management
About version control systems:
- Git, mercurial, svn, bazaar
- Open-source, community-owned
- Some centralized, some decentralized
- Used very widely by coders (over 10 million GitHub repositories)
- Starting to be used by humanists
Version Control is Useful
- Writing papers
- Collaborating
- Text collation
- Analyzing differences between texts
Features:
- Temporarily reset your project to an earlier version:
git checkout <change number>
git revert <change number>
- Combine changesets from two versions:
git merge <branch name>
Version Control is Efficient
Fruit v1.0: apples, oranges, bananas Fruit v1.1: apples, oranges, bananas, pineapples
Ordinary filesystem:
- Fruit1.0.txt, fruit1.1.txt
- Contains redundant copies of apples, oranges, bananas.
Version control system
Git database contains parts:
- A: apples, oranges, bananas
- B: pineapples
Versions are compiled from these parts:
- v1.0: part A
- v1.1: parts A+B
Distributed Version Control
- Doesn’t privilege a central text, repository, user, or computer
- Anyone can pull changes from anyone
- Democratization of scholarly editing?
Version Control Cloud Services
- GitHub: a place for git repositories
- Also BitBucket, GitLab, etc.
- Back up your project in the cloud
- Make it open-source, allowing others to contribute
Project Management
- Issues
- Wiki
- Milestones
- Analysis
Digital Pedagogy in the Humanities
GITenberg
About GITenberg
- ~45,000 books
- Anyone can edit (“crowdsourced”)
- Gitenberg.org
Git-Lit
About Git-Lit
- Open-source initiative to parse and post to GitHub 50,000 works from the British Library
- Parsing ALTO XML, generating Markdown
- Creating beautiful web-based reading editions for each
- Many will be first electronic publications
- Anyone can get involved!
To Get Started
- Edit in the browser!
- Get a command line
- Learn basic commands
- Use plain text formats (Markdown, XML, LaTeX)
- Download git
- Get a GitHub username


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Posts tagged #python
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Django: Internals series - Part 1
Understanding how core components of django works under the hood #django #python
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Python: Generating pdf containing emojis using reportlab
Creating pdf docs that supports emojis. We use "Emojipy" python library to parse emoji's unicode #python #emoji #reportlab
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Understanding regular expression parsing
Understanding how python regular expression parses the given text(input) and matches the pattern #python #regular-expression


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| PREV NEXT | FRAMES NO FRAMES |
| Packages that use MLMethod | |
|---|---|
| org.encog.app.analyst | This package contains the Encog Analyst. |
| org.encog.app.analyst.csv | This package contains all of the classes for directly working with CSV files, used by the analyst. |
| org.encog.mathutil.randomize | |
| org.encog.ml | This package contains all of the classes for Machine Learning. |
| org.encog.ml.factory | This package contains the Encog Machine Learning Factory. |
| org.encog.ml.factory.method | |
| org.encog.ml.factory.train | This package is used to create trainers. |
| org.encog.ml.kmeans | This package holds the classes used to implement kmeans clustering. |
| org.encog.ml.svm | This package contains all of the classes for support vector machines. |
| org.encog.ml.svm.training | This package holds the classes used for SVM training. |
| org.encog.ml.train | |
| org.encog.neural.art | This package contains the classes for ART neural networks. |
| org.encog.neural.bam | THis package contains the classes for BAM networks. |
| org.encog.neural.cpn | This package contains the classes for CPN networks. |
| org.encog.neural.cpn.training | This package contains the instar and outstar training for the network. |
| org.encog.neural.neat | |
| org.encog.neural.neat.training | |
| org.encog.neural.networks | This package contains the neural network machine learning methods. |
| org.encog.neural.networks.structure | This package contains classes used to handle the structure of a neural network. |
| org.encog.neural.networks.training.cross | This package provides cross validation training. |
| org.encog.neural.networks.training.genetic | This package provides genetic training for neural networks. |
| org.encog.neural.networks.training.lma | This package provides Levenberg Marquardt training for neural networks. |
| org.encog.neural.networks.training.pnn | This package provides PNN training. |
| org.encog.neural.networks.training.propagation | This package provides propagation training for neural networks. |
| org.encog.neural.networks.training.simple | This package provides simple ADALINE training. |
| org.encog.neural.pattern | This package contains many helper classes to create neural network types. |
| org.encog.neural.pnn | This package contains the classes for the PNN. |
| org.encog.neural.rbf | This package contains classes for RBF networks. |
| org.encog.neural.som | This package contains classes for the SOM. |
| org.encog.neural.som.training.clustercopy | This package provides a very simple trianing method for SOM's. |
| org.encog.neural.thermal | This package implements thermal neural networks, such as Hopfield and Boltzmann machines. |
| org.encog.plugin | This package holds classes to implement plugins. |
| org.encog.plugin.system | This package holds the system plugins. |
| org.encog.util.simple | |
| org.encog.util.validate |
| Uses of MLMethod in org.encog.app.analyst |
|---|
| Methods in org.encog.app.analyst that return MLMethod | |
|---|---|
MLMethod |
EncogAnalyst.getMethod()
|
| Methods in org.encog.app.analyst with parameters of type MLMethod | |
|---|---|
void |
EncogAnalyst.setMethod(MLMethod method)
|
| Uses of MLMethod in org.encog.app.analyst.csv |
|---|
| Methods in org.encog.app.analyst.csv with parameters of type MLMethod | |
|---|---|
void |
AnalystEvaluateCSV.process(File outputFile,
MLMethod method)
Process the file. |
| Uses of MLMethod in org.encog.mathutil.randomize |
|---|
| Methods in org.encog.mathutil.randomize with parameters of type MLMethod | |
|---|---|
void |
Randomizer.randomize(MLMethod network)
Randomize the synapses and bias values in the basic network based on an array, modify the array. |
void |
NguyenWidrowRandomizer.randomize(MLMethod method)
The Nguyen-Widrow initialization algorithm is the following : 1. |
void |
BasicRandomizer.randomize(MLMethod method)
Randomize the synapses and biases in the basic network based on an array, modify the array. |
| Uses of MLMethod in org.encog.ml |
|---|
| Subinterfaces of MLMethod in org.encog.ml | |
|---|---|
interface |
MLAutoAssocation
Defines a MLMethod that can handle autoassocation. |
interface |
MLClassification
This interface defines a MLMethod that is used for classification. |
interface |
MLClustering
A machine learning method that is used to break data into clusters. |
interface |
MLContext
Defines a MLMethod that can hold context. |
interface |
MLEncodable
Defines a Machine Learning Method that can be encoded to a double array. |
interface |
MLError
Defines Machine Learning Method that can calculate an error based on a data set. |
interface |
MLInput
Defines a MLMethod that accepts input. |
interface |
MLInputOutput
This is a convenience interface that combines MLInput and MLOutput. |
interface |
MLOutput
Defines a MLMethod that produces output. |
interface |
MLProperties
Defines a Machine Learning Method that holds properties. |
interface |
MLRegression
Defines a Machine Learning Method that supports regression. |
interface |
MLResettable
Defines a Machine Learning Method that can be reset to an untrained starting point. |
| Classes in org.encog.ml that implement MLMethod | |
|---|---|
class |
BasicML
A class that provides basic property functionality for the MLProperties interface. |
| Uses of MLMethod in org.encog.ml.factory |
|---|
| Methods in org.encog.ml.factory that return MLMethod | |
|---|---|
MLMethod |
MLMethodFactory.create(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method. |
| Methods in org.encog.ml.factory with parameters of type MLMethod | |
|---|---|
MLTrain |
MLTrainFactory.create(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer. |
| Uses of MLMethod in org.encog.ml.factory.method |
|---|
| Methods in org.encog.ml.factory.method that return MLMethod | |
|---|---|
MLMethod |
PNNFactory.create(String architecture,
int input,
int output)
Create a PNN network. |
MLMethod |
SOMFactory.create(String architecture,
int input,
int output)
Create a SOM. |
MLMethod |
SRNFactory.create(String architecture,
int input,
int output)
Create the SRN. |
MLMethod |
SVMFactory.create(String architecture,
int input,
int output)
Create the SVM. |
MLMethod |
FeedforwardFactory.create(String architecture,
int input,
int output)
Create a feed forward network. |
MLMethod |
RBFNetworkFactory.create(String architecture,
int input,
int output)
Create a RBF network. |
| Uses of MLMethod in org.encog.ml.factory.train |
|---|
| Methods in org.encog.ml.factory.train with parameters of type MLMethod | |
|---|---|
MLTrain |
RPROPFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a RPROP trainer. |
MLTrain |
BackPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a backpropagation trainer. |
MLTrain |
ManhattanFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a Manhattan trainer. |
MLTrain |
GeneticFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer. |
MLTrain |
SCGFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a SCG trainer. |
MLTrain |
SVMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer. |
MLTrain |
SVMSearchFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a SVM trainer. |
MLTrain |
AnnealFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create an annealing trainer. |
MLTrain |
PNNTrainFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a PNN trainer. |
MLTrain |
LMAFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer. |
MLTrain |
ClusterSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a cluster SOM trainer. |
MLTrain |
QuickPropFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a quick propagation trainer. |
MLTrain |
RBFSVDFactory.create(MLMethod method,
MLDataSet training,
String args)
Create a RBF-SVD trainer. |
MLTrain |
NeighborhoodSOMFactory.create(MLMethod method,
MLDataSet training,
String argsStr)
Create a LMA trainer. |
| Uses of MLMethod in org.encog.ml.kmeans |
|---|
| Classes in org.encog.ml.kmeans that implement MLMethod | |
|---|---|
class |
KMeansClustering
This class performs a basic K-Means clustering. |
| Uses of MLMethod in org.encog.ml.svm |
|---|
| Classes in org.encog.ml.svm that implement MLMethod | |
|---|---|
class |
SVM
This is a network that is backed by one or more Support Vector Machines (SVM). |
| Uses of MLMethod in org.encog.ml.svm.training |
|---|
| Methods in org.encog.ml.svm.training that return MLMethod | |
|---|---|
MLMethod |
SVMSearchTrain.getMethod()
Get the current best machine learning method from the training. |
MLMethod |
SVMTrain.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.ml.train |
|---|
| Methods in org.encog.ml.train that return MLMethod | |
|---|---|
MLMethod |
MLTrain.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.art |
|---|
| Classes in org.encog.neural.art that implement MLMethod | |
|---|---|
class |
ART
Adaptive Resonance Theory (ART) is a form of neural network developed by Stephen Grossberg and Gail Carpenter. |
class |
ART1
Implements an ART1 neural network. |
| Uses of MLMethod in org.encog.neural.bam |
|---|
| Classes in org.encog.neural.bam that implement MLMethod | |
|---|---|
class |
BAM
Bidirectional associative memory (BAM) is a type of neural network developed by Bart Kosko in 1988. |
| Uses of MLMethod in org.encog.neural.cpn |
|---|
| Classes in org.encog.neural.cpn that implement MLMethod | |
|---|---|
class |
CPN
Counterpropagation Neural Networks (CPN) were developed by Professor Robert Hecht-Nielsen in 1987. |
| Uses of MLMethod in org.encog.neural.cpn.training |
|---|
| Methods in org.encog.neural.cpn.training that return MLMethod | |
|---|---|
MLMethod |
TrainOutstar.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.neat |
|---|
| Classes in org.encog.neural.neat that implement MLMethod | |
|---|---|
class |
NEATNetwork
Implements a NEAT network as a synapse between two layers. |
| Uses of MLMethod in org.encog.neural.neat.training |
|---|
| Methods in org.encog.neural.neat.training that return MLMethod | |
|---|---|
MLMethod |
NEATTraining.getMethod()
|
| Uses of MLMethod in org.encog.neural.networks |
|---|
| Subinterfaces of MLMethod in org.encog.neural.networks | |
|---|---|
interface |
ContainsFlat
Interface that specifies that a machine learning method contains a flat network. |
| Classes in org.encog.neural.networks that implement MLMethod | |
|---|---|
class |
BasicNetwork
This class implements a neural network. |
| Uses of MLMethod in org.encog.neural.networks.structure |
|---|
| Methods in org.encog.neural.networks.structure with parameters of type MLMethod | |
|---|---|
static void |
NetworkCODEC.arrayToNetwork(double[] array,
MLMethod network)
Use an array to populate the memory of the neural network. |
static int |
NetworkCODEC.networkSize(MLMethod network)
Determine the network size. |
static double[] |
NetworkCODEC.networkToArray(MLMethod network)
Convert to an array. |
| Uses of MLMethod in org.encog.neural.networks.training.cross |
|---|
| Methods in org.encog.neural.networks.training.cross that return MLMethod | |
|---|---|
MLMethod |
CrossTraining.getMethod()
Get the current best machine learning method from the training. |
| Constructors in org.encog.neural.networks.training.cross with parameters of type MLMethod | |
|---|---|
CrossTraining(MLMethod network,
FoldedDataSet training)
Construct a cross trainer. |
| Uses of MLMethod in org.encog.neural.networks.training.genetic |
|---|
| Methods in org.encog.neural.networks.training.genetic that return MLMethod | |
|---|---|
MLMethod |
NeuralGeneticAlgorithm.getMethod()
Get the current best machine learning method from the training. |
MLMethod |
NeuralGeneticAlgorithm.NeuralGeneticAlgorithmHelper.getMethod()
Get the current best neural network. |
| Uses of MLMethod in org.encog.neural.networks.training.lma |
|---|
| Methods in org.encog.neural.networks.training.lma that return MLMethod | |
|---|---|
MLMethod |
LevenbergMarquardtTraining.getMethod()
|
| Uses of MLMethod in org.encog.neural.networks.training.pnn |
|---|
| Methods in org.encog.neural.networks.training.pnn that return MLMethod | |
|---|---|
MLMethod |
TrainBasicPNN.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.networks.training.propagation |
|---|
| Methods in org.encog.neural.networks.training.propagation that return MLMethod | |
|---|---|
MLMethod |
Propagation.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.networks.training.simple |
|---|
| Methods in org.encog.neural.networks.training.simple that return MLMethod | |
|---|---|
MLMethod |
TrainAdaline.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.pattern |
|---|
| Methods in org.encog.neural.pattern that return MLMethod | |
|---|---|
MLMethod |
ElmanPattern.generate()
Generate the Elman neural network. |
MLMethod |
HopfieldPattern.generate()
Generate the Hopfield neural network. |
MLMethod |
BoltzmannPattern.generate()
Generate the network. |
MLMethod |
JordanPattern.generate()
Generate a Jordan neural network. |
MLMethod |
PNNPattern.generate()
Generate the RSOM network. |
MLMethod |
SOMPattern.generate()
Generate the RSOM network. |
MLMethod |
RadialBasisPattern.generate()
Generate the RBF network. |
MLMethod |
ADALINEPattern.generate()
Generate the network. |
MLMethod |
SVMPattern.generate()
|
MLMethod |
NeuralNetworkPattern.generate()
Generate the specified neural network. |
MLMethod |
ART1Pattern.generate()
Generate the neural network. |
MLMethod |
BAMPattern.generate()
|
MLMethod |
CPNPattern.generate()
Generate the network. |
MLMethod |
FeedForwardPattern.generate()
Generate the feedforward neural network. |
| Uses of MLMethod in org.encog.neural.pnn |
|---|
| Classes in org.encog.neural.pnn that implement MLMethod | |
|---|---|
class |
AbstractPNN
Abstract class to build PNN networks upon. |
class |
BasicPNN
This class implements either a: Probabilistic Neural Network (PNN) General Regression Neural Network (GRNN) To use a PNN specify an output mode of classification, to make use of a GRNN specify either an output mode of regression or un-supervised autoassociation. |
| Uses of MLMethod in org.encog.neural.rbf |
|---|
| Classes in org.encog.neural.rbf that implement MLMethod | |
|---|---|
class |
RBFNetwork
RBF neural network. |
| Uses of MLMethod in org.encog.neural.som |
|---|
| Classes in org.encog.neural.som that implement MLMethod | |
|---|---|
class |
SOM
A self organizing map neural network. |
| Uses of MLMethod in org.encog.neural.som.training.clustercopy |
|---|
| Methods in org.encog.neural.som.training.clustercopy that return MLMethod | |
|---|---|
MLMethod |
SOMClusterCopyTraining.getMethod()
Get the current best machine learning method from the training. |
| Uses of MLMethod in org.encog.neural.thermal |
|---|
| Classes in org.encog.neural.thermal that implement MLMethod | |
|---|---|
class |
BoltzmannMachine
Implements a Boltzmann machine. |
class |
HopfieldNetwork
Implements a Hopfield network. |
class |
ThermalNetwork
The thermal network forms the base class for Hopfield and Boltzmann machines. |
| Uses of MLMethod in org.encog.plugin |
|---|
| Methods in org.encog.plugin that return MLMethod | |
|---|---|
MLMethod |
EncogPluginService1.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method. |
| Methods in org.encog.plugin with parameters of type MLMethod | |
|---|---|
MLTrain |
EncogPluginService1.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer. |
| Uses of MLMethod in org.encog.plugin.system |
|---|
| Methods in org.encog.plugin.system that return MLMethod | |
|---|---|
MLMethod |
SystemActivationPlugin.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method. |
MLMethod |
SystemMethodsPlugin.createMethod(String methodType,
String architecture,
int input,
int output)
Create a new machine learning method. |
MLMethod |
SystemTrainingPlugin.createMethod(String methodType,
String architecture,
int input,
int output)
|
| Methods in org.encog.plugin.system with parameters of type MLMethod | |
|---|---|
MLTrain |
SystemActivationPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer. |
MLTrain |
SystemMethodsPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
Create a trainer. |
MLTrain |
SystemTrainingPlugin.createTraining(MLMethod method,
MLDataSet training,
String type,
String args)
|
| Uses of MLMethod in org.encog.util.simple |
|---|
| Methods in org.encog.util.simple with parameters of type MLMethod | |
|---|---|
static void |
EncogUtility.trainToError(MLMethod method,
MLDataSet dataSet,
double error)
Train the method, to a specific error, send the output to the console. |
| Uses of MLMethod in org.encog.util.validate |
|---|
| Methods in org.encog.util.validate with parameters of type MLMethod | |
|---|---|
static void |
ValidateNetwork.validateMethodToData(MLMethod method,
MLDataSet training)
|
|
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| PREV NEXT | FRAMES NO FRAMES |


?PLANTS
OF
BAHAMA
ISLANDS
Proserpinaea
palustris
L*
North
Andros:
in
water
of
marsh
along
Queen's
Highway,
3*5
miles
due
west
of
Nicolis
Town*
December
10,
1976
d.s.Correli
&
G.R.Proctor
U7…9…
Herbarium
FAIRCHILD
TROPICAL
GARDEN
Proserpinaca
palustris
L.
var.
palustris
Determined
by
Dr.
P.M.
Catling
-
May
2002
Agriculture
&
Agri-Food
Canada
DAO
Herbarium
NEW
YORK
BOTANICAL
GARDEN
500429
01500429
Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
low
undetermined
medium
high
very high


| Home | Trees | Indices | Help |
|---|
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Is a container to other classes and methods exclusive of the domain. Instances from Domain Class contain the attributes that define characteristics of the simulation such as regions, meshing, and boundary conditions. They are somehow independent of the details regarding the solution of the problem. A domain is one of the principal components of the Simulation Class. To be included here are instances of clases such as: - Regions. - Nodes. - Elements. - Boundaries. These are the main attibutes of a domain in Finite Elemen Analysis. :ivar nodes: Nodes() After discretization space is reduced to points known as nodes, each having unique coordinates. Nodes, is a class that contains the coordinates of each node. for a FE model. :ivar elements: Elements() The way in which FEA solves problems is by solving differential equations over chunks of the domain. These chunks are called elements, and as the name suggest they are characterized by being finite. By defining these elements as simple interpolation function of nodes, one can create an approximate representation of space and solve the differential equations for these functions. elements is an instance that contains information about the elemens that form the domain. :ivar regions: list [ Region1, Region2 ] regions is a list of instances of class region which will help us relate material properties with different sets of elements. :ivar boundaries: Boundaries() As in any situation where one has to solve differential equations, in FEA particular conditions have to be stated in some boundaries of the domain in order to have unique solutions. This instance has information about which boundaries have boundary conditions that are relevant to the solution of the simulation.
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This init is not very usefull. Maybe it shouldnt be there |
Reads boundary confitions attributes from a .bc file. .bc files are made by hand by the user, and by means of tags relate line elements to pre-established values. There are three kinds of boundary conditions D: Dirichlet Conditions where one knows the value of the solution at the node. N: Newman Conditions where one knows the value of the derivative of the solution at the node. B: Bloch Made for Bloch periodicity :param filename: str which contains path and filename of input .bc file. |
Reads mesh characteristics from a .msh file of gmsh ASCII format V2.2. This method is meant to populate a domain instance by reading nodes and elements from a .msh file. - Instances from Nodes and Elements are created. - Function read_mesh() from module read_mesh.py is used to extract raw sets of elements and nodes. - raw sets are added to nodes and elements by using their method add() :param filename: str which contains path and filename of input .msh file. :keyord vectorial: Boolean Tells if the problem is to be solved with a scalar or vectorial formulation. vectorial = True, builds two unknowns for each node. |
Reads Region attributes from a .reg file exported by pickle. This method is meant to populate a domain instance by reading regions attributes from a .reg file. The .reg file is a list of instances of class Region() that has been exported by using python's pickle function. A .reg file contains elements, material properties, and a tag. - Import the list - For each region in the list, add the elemtns that have its tag by deep-copying them. :param filename: str which contains path and filename of input .reg file. |
| Home | Trees | Indices | Help |
|---|
| Generated by Epydoc 3.0.1 on Sun May 5 17:26:48 2013 | http://epydoc.sourceforge.net |


| California Crime
Rates 1960 -
2015
|
| California Population
and Number of
Crimes
1960 -
2015 |
|
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1960 |
15,717,204 |
546,069 |
37,558 |
508,511 |
616 |
2,859 |
15,287 |
18,796 |
143,102 |
311,956 |
53,453 |
| 1961 |
16,397,000 |
559,162 |
38,154 |
521,008 |
605 |
3,033 |
14,832 |
19,684 |
146,615 |
319,834 |
54,559 |
| 1962 |
16,970,000 |
604,297 |
39,846 |
564,451 |
657 |
2,946 |
15,598 |
20,645 |
158,523 |
348,373 |
57,555 |
| 1963 |
17,590,000 |
666,128 |
42,419 |
623,709 |
673 |
3,080 |
16,458 |
22,208 |
175,703 |
384,289 |
63,717 |
| 1964 |
18,084,000 |
748,656 |
48,026 |
700,630 |
740 |
3,621 |
18,667 |
24,998 |
196,883 |
427,960 |
75,787 |
| 1965 |
18,602,000 |
803,487 |
52,490 |
750,997 |
880 |
3,948 |
21,081 |
26,581 |
225,007 |
444,217 |
81,773 |
| 1966 |
18,918,000 |
860,662 |
57,718 |
802,944 |
868 |
4,432 |
22,317 |
30,101 |
241,666 |
474,122 |
87,156 |
| 1967 |
19,153,000 |
968,203 |
67,446 |
900,757 |
1,039 |
4,792 |
28,539 |
33,076 |
276,958 |
526,477 |
97,322 |
| 1968 |
19,221,000 |
1,099,656 |
81,293 |
1,018,363 |
1,150 |
5,744 |
36,991 |
37,408 |
316,081 |
582,838 |
119,444 |
| 1969 |
19,443,000 |
1,185,961 |
89,878 |
1,096,083 |
1,386 |
7,053 |
39,240 |
42,199 |
325,891 |
638,249 |
131,943 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1970 |
19,953,134 |
1,264,854 |
94,741 |
1,170,113 |
1,376 |
7,005 |
41,277 |
45,083 |
349,788 |
682,811 |
137,514 |
| 1971 |
20,223,000 |
1,352,941 |
104,872 |
1,248,069 |
1,642 |
7,300 |
47,626 |
48,304 |
392,277 |
711,417 |
144,375 |
| 1972 |
20,468,000 |
1,312,635 |
110,667 |
1,201,968 |
1,791 |
8,127 |
48,829 |
51,920 |
398,960 |
663,568 |
139,440 |
| 1973 |
20,601,000 |
1,298,872 |
116,563 |
1,182,309 |
1,862 |
8,357 |
49,531 |
56,813 |
407,824 |
643,488 |
130,997 |
| 1974 |
20,907,000 |
1,431,468 |
127,658 |
1,303,810 |
1,985 |
8,494 |
52,822 |
64,357 |
433,194 |
737,067 |
133,549 |
| 1975 |
21,185,000 |
1,526,293 |
138,842 |
1,387,451 |
2,209 |
8,807 |
59,827 |
67,999 |
469,726 |
784,639 |
133,086 |
| 1976 |
21,520,000 |
1,556,757 |
144,041 |
1,412,716 |
2,220 |
9,614 |
59,318 |
72,889 |
467,980 |
806,086 |
138,650 |
| 1977 |
21,896,000 |
1,534,621 |
154,582 |
1,380,039 |
2,515 |
10,825 |
62,852 |
78,390 |
468,452 |
766,317 |
145,270 |
| 1978 |
22,294,000 |
1,586,483 |
165,626 |
1,420,857 |
2,611 |
11,316 |
68,235 |
83,464 |
488,966 |
777,783 |
154,108 |
| 1979 |
22,696,000 |
1,695,108 |
184,087 |
1,511,021 |
2,952 |
12,239 |
75,767 |
93,129 |
496,310 |
847,148 |
167,563 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1980 |
23,532,680 |
1,843,332 |
210,290 |
1,633,042 |
3,411 |
13,693 |
90,420 |
102,766 |
545,138 |
913,070 |
174,834 |
| 1981 |
24,159,000 |
1,833,787 |
208,485 |
1,625,302 |
3,143 |
13,566 |
93,781 |
97,995 |
540,806 |
921,939 |
162,557 |
| 1982 |
24,724,000 |
1,801,256 |
201,429 |
1,599,827 |
2,779 |
12,529 |
91,988 |
94,133 |
499,466 |
935,831 |
164,530 |
| 1983 |
25,174,000 |
1,680,978 |
194,491 |
1,486,487 |
2,639 |
12,093 |
85,826 |
93,933 |
460,460 |
867,123 |
158,904 |
| 1984 |
25,622,000 |
1,657,320 |
195,589 |
1,461,731 |
2,717 |
11,702 |
83,924 |
97,246 |
443,094 |
857,328 |
161,309 |
| 1985 |
26,365,000 |
1,718,473 |
201,763 |
1,516,710 |
2,770 |
11,421 |
86,387 |
101,185 |
448,506 |
890,967 |
177,237 |
| 1986 |
26,981,000 |
1,824,669 |
248,370 |
1,576,299 |
3,038 |
12,119 |
92,512 |
140,701 |
457,698 |
913,004 |
205,597 |
| 1987 |
27,663,000 |
1,799,871 |
253,943 |
1,545,928 |
2,924 |
12,109 |
83,341 |
155,569 |
419,969 |
896,335 |
229,624 |
| 1988 |
28,168,000 |
1,869,092 |
261,912 |
1,607,180 |
2,936 |
11,780 |
86,141 |
161,055 |
407,631 |
933,636 |
265,913 |
| 1989 |
29,063,000 |
1,965,652 |
284,136 |
1,681,516 |
3,158 |
11,966 |
96,431 |
172,581 |
410,468 |
972,603 |
298,445 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1990 |
29,760,021 |
1,965,237 |
311,051 |
1,654,186 |
3,553 |
12,688 |
112,208 |
182,602 |
400,392 |
951,580 |
302,214 |
| 1991 |
30,380,000 |
2,057,513 |
331,122 |
1,726,391 |
3,859 |
12,896 |
124,939 |
189,428 |
424,656 |
986,120 |
315,615 |
| 1992 |
30,867,000 |
2,061,761 |
345,624 |
1,716,137 |
3,921 |
12,761 |
130,897 |
198,045 |
427,491 |
968,534 |
320,112 |
| 1993 |
31,211,000 |
2,015,265 |
336,381 |
1,678,884 |
4,096 |
11,766 |
126,436 |
194,083 |
414,182 |
945,407 |
319,295 |
| 1994 |
31,431,000 |
1,940,497 |
318,395 |
1,622,102 |
3,703 |
10,984 |
112,160 |
191,548 |
384,257 |
929,640 |
308,205 |
| 1995 |
31,589,000 |
1,841,984 |
305,154 |
1,536,830 |
3,531 |
10,554 |
104,611 |
186,458 |
353,895 |
902,456 |
280,479 |
| 1996 |
31,878,000 |
1,660,131 |
274,996 |
1,385,135 |
2,916 |
10,244 |
94,222 |
167,614 |
312,212 |
830,457 |
242,466 |
| 1997 |
32,268,000 |
1,569,949 |
257,582 |
1,312,367 |
2,579 |
10,189 |
81,468 |
163,346 |
299,240 |
784,405 |
228,722 |
| 1998 |
32,667,000 |
1,418,674 |
229,883 |
1,188,791 |
2,171 |
9,782 |
68,782 |
149,148 |
269,012 |
724,262 |
195,517 |
| 1999 |
33,145,121 |
1,261,164 |
207,879 |
1,053,285 |
2,005 |
9,363 |
60,039 |
136,472 |
223,814 |
660,991 |
168,480 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 2000 |
33,871,648 |
1,266,714 |
210,531 |
1,056,183 |
2,079 |
9,785 |
60,249 |
138,418 |
222,293 |
651,855 |
182,035 |
| 2001 |
34,600,463 |
1,347,056 |
212,867 |
1,134,189 |
2,206 |
9,960 |
64,614 |
136,087 |
232,273 |
697,739 |
204,177 |
| 2002 |
35,001,986 |
1,384,872 |
208,388 |
1,176,484 |
2,395 |
10,198 |
64,968 |
130,827 |
238,4280 |
715,692 |
222,364 |
| 2003 |
35,462,712 |
1,420,637 |
205,551 |
1,215,086 |
2,407 |
9,994 |
63,770 |
129,380 |
242,272 |
731,486 |
241,326 |
| 2004 |
35,842,038 |
1,416,369 |
189,175 |
1,227,194 |
2,392 |
9,615 |
61,768 |
115,400 |
245,903 |
728,687 |
252,604 |
| 2005 |
36,154,147 |
1,390,710 |
190,178 |
1,200,532 |
2,503 |
9,392 |
63,622 |
114,661 |
250,521 |
692,467 |
257,543 |
| 2006 |
36,457,549 |
1,350,137 |
194,120 |
1,156,017 |
2,485 |
9,212 |
70,968 |
111,455 |
246,464 |
666,860 |
242,693 |
| 2007 |
36,553,213 |
1,299,685 |
191,025 |
1,108,660 |
2,260 |
9,013 |
70,542 |
109,210 |
237,025 |
652,243 |
219,392 |
| 2008 |
36,580,371 |
1,265,939 |
185,173 |
1,080,766 |
2,142 |
8,903 |
69,385 |
104,743 |
237,724 |
650,513 |
192,529 |
| 2009 |
36,961,664 |
1,184,367 |
174,934 |
1,009,433 |
1,972 |
8,713 |
64,093 |
100,156 |
230,198 |
615,402 |
163,833 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 2010 |
37,338,198 |
1,146,072 |
164,133 |
981,939 |
1,809 |
8,331 |
58,116 |
95,877 |
228,857 |
600,558 |
152,524 |
| 2011 |
37,683,933 |
1,128,765 |
154,943 |
973,822 |
1,792 |
7,665 |
54,291 |
91,195 |
230,075 |
596,905 |
146,842 |
| 2012
|
37,999,878
|
1,210,409
|
160,944
|
1,049,465
|
1,884
|
7,837
|
56,521
|
94,702
|
245,767
|
635,090
|
168,608
|
| 2013
|
38,431,393
|
1,173,646
|
154,739
|
1,018,907
|
1,746
|
7,464
|
53,640
|
89,029
|
232,058
|
621,557
|
165,292
|
| 2014
|
38,792,291
|
1,100,956
|
153,763
|
947,193
|
1,700
|
8,389
|
48,681
|
91,804
|
202,669
|
592,673
|
151,851
|
| 2015
|
39,144,818
|
1,191,797
|
166,883
|
1,024,914
|
1,861
|
9,387
|
52,862
|
99,349
|
197,404
|
656,517
|
170,993
|
|
|
|
Compiled
by the DisasterCenter.com
From:
FBI UCS Annual Crime Reports |
The
Uniform Crime
Reporting
(UCR) Program is the starting place
for those seeking
information on crime in the nation. The UCR is produced from data
received from over 18,000 law enforcement agencies voluntarily
participating
in the program. . |
|
|
| California Population
and Rate of Crime per
100,000 People
1960 -
2015 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
rape |
Robbery |
Assault |
Burglary |
Theft |
Theft |
| 1960 |
15,717,204 |
3,474.3 |
239.0 |
3,235.4 |
3.9 |
18.2 |
97.3 |
119.6 |
910.5 |
1,984.8 |
340.1 |
| 1961 |
16,397,000 |
3,410.1 |
232.7 |
3,177.5 |
3.7 |
18.5 |
90.5 |
120.0 |
894.2 |
1,950.6 |
332.7 |
| 1962 |
16,970,000 |
3,561.0 |
234.8 |
3,326.2 |
3.9 |
17.4 |
91.9 |
121.7 |
934.1 |
2,052.9 |
339.2 |
| 1963 |
17,590,000 |
3,787.0 |
241.2 |
3,545.8 |
3.8 |
17.5 |
93.6 |
126.3 |
998.9 |
2,184.7 |
362.2 |
| 1964 |
18,084,000 |
4,139.9 |
265.6 |
3,874.3 |
4.1 |
20.0 |
103.2 |
138.2 |
1,088.7 |
2,366.5 |
419.1 |
| 1965 |
18,602,000 |
4,319.4 |
282.2 |
4,037.2 |
4.7 |
21.2 |
113.3 |
142.9 |
1,209.6 |
2,388.0 |
439.6 |
| 1966 |
18,918,000 |
4,549.4 |
305.1 |
4,244.3 |
4.6 |
23.4 |
118.0 |
159.1 |
1,277.4 |
2,506.2 |
460.7 |
| 1967 |
19,153,000 |
5,055.1 |
352.1 |
4,703.0 |
5.4 |
25.0 |
149.0 |
172.7 |
1,446.0 |
2,748.8 |
508.1 |
| 1968 |
19,221,000 |
5,721.1 |
422.9 |
5,298.2 |
6.0 |
29.9 |
192.5 |
194.6 |
1,644.5 |
3,032.3 |
621.4 |
| 1969 |
19,443,000 |
6,099.7 |
462.3 |
5,637.4 |
7.1 |
36.3 |
201.8 |
217.0 |
1,676.1 |
3,282.7 |
678.6 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1970 |
19,953,134 |
6,339.1 |
474.8 |
5,864.3 |
6.9 |
35.1 |
206.9 |
225.9 |
1,753.0 |
3,422.1 |
689.2 |
| 1971 |
20,223,000 |
6,690.1 |
518.6 |
6,171.5 |
8.1 |
36.1 |
235.5 |
238.9 |
1,939.8 |
3,517.9 |
713.9 |
| 1972 |
20,468,000 |
6,413.1 |
540.7 |
5,872.4 |
8.8 |
39.7 |
238.6 |
253.7 |
1,949.2 |
3,242.0 |
681.3 |
| 1973 |
20,601,000 |
6,304.9 |
565.8 |
5,739.1 |
9.0 |
40.6 |
240.4 |
275.8 |
1,979.6 |
3,123.6 |
635.9 |
| 1974 |
20,907,000 |
6,846.8 |
610.6 |
6,236.2 |
9.5 |
40.6 |
252.7 |
307.8 |
2,072.0 |
3,525.5 |
638.8 |
| 1975 |
21,185,000 |
7,204.6 |
655.4 |
6,549.2 |
10.4 |
41.6 |
282.4 |
321.0 |
2,217.3 |
3,703.7 |
628.2 |
| 1976 |
21,520,000 |
7,234.0 |
669.3 |
6,564.7 |
10.3 |
44.7 |
275.6 |
338.7 |
2,174.6 |
3,745.8 |
644.3 |
| 1977 |
21,896,000 |
7,008.7 |
706.0 |
6,302.7 |
11.5 |
49.4 |
287.0 |
358.0 |
2,139.4 |
3,499.8 |
663.5 |
| 1978 |
22,294,000 |
7,116.2 |
742.9 |
6,373.3 |
11.7 |
50.8 |
306.1 |
374.4 |
2,193.3 |
3,488.8 |
691.3 |
| 1979 |
22,696,000 |
7,468.8 |
811.1 |
6,657.7 |
13.0 |
53.9 |
333.8 |
410.3 |
2,186.8 |
3,732.6 |
738.3 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1980 |
23,532,680 |
7,833.1 |
893.6 |
6,939.5 |
14.5 |
58.2 |
384.2 |
436.7 |
2,316.5 |
3,880.0 |
742.9 |
| 1981 |
24,159,000 |
7,590.5 |
863.0 |
6,727.5 |
13.0 |
56.2 |
388.2 |
405.6 |
2,238.5 |
3,816.1 |
672.9 |
| 1982 |
24,724,000 |
7,285.5 |
814.7 |
6,470.7 |
11.2 |
50.7 |
372.1 |
380.7 |
2,020.2 |
3,785.1 |
665.5 |
| 1983 |
25,174,000 |
6,677.4 |
772.6 |
5,904.9 |
10.5 |
48.0 |
340.9 |
373.1 |
1,829.1 |
3,444.5 |
631.2 |
| 1984 |
25,622,000 |
6,468.3 |
763.4 |
5,705.0 |
10.6 |
45.7 |
327.5 |
379.5 |
1,729.3 |
3,346.1 |
629.6 |
| 1985 |
26,365,000 |
6,518.0 |
765.3 |
5,752.7 |
10.5 |
43.3 |
327.7 |
383.8 |
1,701.1 |
3,379.4 |
672.2 |
| 1986 |
26,981,000 |
6,762.8 |
920.5 |
5,842.3 |
11.3 |
44.9 |
342.9 |
521.5 |
1,696.4 |
3,383.9 |
762.0 |
| 1987 |
27,663,000 |
6,506.4 |
918.0 |
5,588.4 |
10.6 |
43.8 |
301.3 |
562.4 |
1,518.2 |
3,240.2 |
830.1 |
| 1988 |
28,168,000 |
6,635.5 |
929.8 |
5,705.7 |
10.4 |
41.8 |
305.8 |
571.8 |
1,447.1 |
3,314.5 |
944.0 |
| 1989 |
29,063,000 |
6,763.4 |
977.7 |
5,785.8 |
10.9 |
41.2 |
331.8 |
593.8 |
1,412.3 |
3,346.5 |
1,026.9 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 1990 |
29,760,021 |
6,603.6 |
1,045.2 |
5,558.4 |
11.9 |
42.6 |
377.0 |
613.6 |
1,345.4 |
3,197.5 |
1,015.5 |
| 1991 |
30,380,000 |
6,772.6 |
1,089.9 |
5,682.7 |
12.7 |
42.4 |
411.3 |
623.5 |
1,397.8 |
3,246.0 |
1,038.9 |
| 1992 |
30,867,000 |
6,679.5 |
1,119.7 |
5,559.8 |
12.7 |
41.3 |
424.1 |
641.6 |
1,384.9 |
3,137.8 |
1,037.1 |
| 1993 |
31,211,000 |
6,456.9 |
1,077.8 |
5,379.1 |
13.1 |
37.7 |
405.1 |
621.8 |
1,327.0 |
3,029.1 |
1,023.0 |
| 1994 |
31,431,000 |
6,173.8 |
1,013.0 |
5,160.8 |
11.8 |
34.9 |
356.8 |
609.4 |
1,222.5 |
2,957.7 |
980.6 |
| 1995 |
31,589,000 |
5,831.1 |
966.0 |
4,865.1 |
11.2 |
33.4 |
331.2 |
590.3 |
1,120.3 |
2,856.9 |
887.9 |
| 1996 |
31,878,000 |
5,207.8 |
862.7 |
4,345.1 |
9.1 |
32.1 |
295.6 |
525.8 |
979.4 |
2,605.1 |
760.6 |
| 1997 |
32,268,000 |
4,865.3 |
798.3 |
4,067.1 |
8.0 |
31.6 |
252.5 |
506.2 |
927.4 |
2,430.9 |
708.8 |
| 1998 |
32,667,000 |
4,342.8 |
703.7 |
3,639.1 |
6.6 |
29.9 |
210.6 |
456.6 |
823.5 |
2,217.1 |
598.5 |
| 1999 |
33,145,121 |
3,805.0 |
627.2 |
3,177.8 |
6.0 |
28.2 |
181.1 |
411.7 |
675.3 |
1,994.2 |
508.3 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 2000 |
33,871,648 |
3,739.7 |
621.6 |
3,118.2 |
6.1 |
28.9 |
177.9 |
408.7 |
656.3 |
1,924.5 |
537.4 |
| 2001 |
34,600,463 |
3,893.2 |
615.2 |
3,278.0 |
6.4 |
28.8 |
186.7 |
393.3 |
671.3 |
2,016.6 |
590.1 |
| 2002 |
35,001,986 |
3,956.6 |
595.4 |
3,361.2 |
6.8 |
29.1 |
185.6 |
373.8 |
681.2 |
2,044.7 |
635.3 |
| 2003 |
35,462,712 |
4,006.0 |
579.6 |
3,426.4 |
6.7 |
28.2 |
179.8 |
364.8 |
683.2 |
2,062.7 |
680.5 |
| 2004 |
35,842,038 |
3,970.8 |
527.8 |
3,423.9 |
6.7 |
26.8 |
172.3 |
322.0 |
686.1 |
2,033.1 |
704.8 |
| 2005 |
36,154,147 |
3,846.2 |
526.0 |
3,320.6 |
6.9 |
26.0 |
176.1 |
317.3 |
692.9 |
1,915.3 |
712.3 |
| 2006 |
36,457,549 |
3,743.4 |
532.5 |
3,170.9 |
6.8 |
25.3 |
194.7 |
305.7 |
676.0 |
1,829.1 |
665.7 |
| 2007 |
36,553,213 |
3,555.6 |
522.6 |
3,033.0 |
6.2 |
24.7 |
193.0 |
298.8 |
648.4 |
1,784.4 |
600.2 |
| 2008 |
36,580,371 |
3,460.7 |
506.2 |
2,954.5 |
5.9 |
24.3 |
189.7 |
286.3 |
649.9 |
1,778.3 |
526.3 |
| 2009 |
36,961,664 |
3,204.3 |
473.3 |
2,731.0 |
5.3 |
23.6 |
173.4 |
271.0 |
622.8 |
1,665.0 |
443.3 |
|
|
|
|
|
|
Forcible |
|
Aggravated |
|
Larceny- |
Vehicle |
| Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
assault |
Burglary |
Theft |
Theft |
| 2010 |
37,338,198 |
3,069.5 |
439.6 |
2,629.9 |
4.8 |
22.3 |
155.6 |
256.8 |
612.9 |
1,608.4 |
408.5 |
| 2011 |
37,683,933 |
2,995.4 |
411.2 |
2,584.2 |
4.8 |
20.3 |
144.1 |
242.0 |
610.5 |
1,584.0 |
389.7 |
| 2012
|
37,999,878
|
3,185.3
|
423.5
|
2,761.8
|
5.0
|
20.6
|
148.7
|
249.2
|
646.8
|
1,671.3
|
443.7
|
| 2013
|
38,431,393
|
3,053.8
|
402.6
|
2,651.2
|
4.5
|
19.4
|
139.6
|
231.7
|
603.8
|
1,617.3
|
430.1
|
| 2014
|
38,792,291
|
2,838.1
|
396.4
|
2,441.7
|
4.4
|
21.6
|
125.5
|
236.7
|
522.4
|
1,527.8
|
391.4
|
| 2015
|
39,144,818
|
3,044.6
|
426.3
|
2,618.3
|
4.8
|
24.0
|
135.0
|
253.8
|
504.3
|
1,677.1
|
436.8
|
|
| DisasterCenter.com
developed the following table to rank the rates of
crime in
California over time in relation to the rates of crime of other
States . This new
way to examine fluctuations in the crime rate reveals patterns that are
important for
law enforcement executives, students of criminal justice, researchers,
members of the media, and the public at large seeking information on
crime in the nation..
|
|
The crime
"Index" is
made up of the total number of violent and
property crimes. Since most crime is
property related the crime index gives more weight to property
crime. |
|
|
California Population
and
Rate of Crime
Rank Compared
to other States
|
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
1960 |
1 |
1 |
2 |
2 |
27 |
2 |
2 |
7 |
2 |
2 |
3 |
| California |
1961 |
1 |
2 |
2 |
2 |
26 |
1 |
4 |
6 |
2 |
2 |
4 |
| California |
1962 |
1 |
2 |
2 |
2 |
23 |
4 |
4 |
4 |
1 |
2 |
4 |
| California |
1963 |
1 |
2 |
2 |
2 |
24 |
1 |
4 |
6 |
2 |
2 |
5 |
| California |
1964 |
1 |
2 |
3 |
2 |
24 |
2 |
4 |
7 |
1 |
2 |
3 |
| California |
1965 |
1 |
1 |
6 |
1 |
21 |
2 |
4 |
8 |
1 |
1 |
2 |
| California |
1966 |
1 |
1 |
6 |
1 |
25 |
2 |
5 |
8 |
1 |
2 |
3 |
| California |
1967 |
1 |
1 |
6 |
1 |
23 |
2 |
5 |
5 |
1 |
2 |
3 |
| California |
1968 |
1 |
1 |
5 |
1 |
24 |
2 |
5 |
5 |
1 |
1 |
3 |
| California |
1969 |
1 |
1 |
5 |
1 |
22 |
1 |
5 |
5 |
1 |
1 |
3 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
1970 |
1 |
1 |
5 |
1 |
23 |
2 |
5 |
5 |
1 |
2 |
4 |
| California |
1971 |
1 |
1 |
5 |
1 |
21 |
3 |
5 |
7 |
1 |
2 |
3 |
| California |
1972 |
1 |
1 |
5 |
1 |
19 |
2 |
5 |
7 |
1 |
2 |
3 |
| California |
1973 |
1 |
3 |
6 |
3 |
19 |
3 |
6 |
7 |
2 |
3 |
3 |
| California |
1974 |
1 |
4 |
7 |
4 |
23 |
3 |
7 |
6 |
4 |
5 |
4 |
| California |
1975 |
1 |
4 |
7 |
4 |
19 |
3 |
6 |
8 |
4 |
7 |
6 |
| California |
1976 |
1 |
3 |
3 |
3 |
15 |
3 |
6 |
5 |
3 |
7 |
5 |
| California |
1977 |
1 |
3 |
3 |
5 |
8 |
2 |
4 |
4 |
3 |
8 |
5 |
| California |
1978 |
1 |
4 |
4 |
4 |
8 |
3 |
4 |
4 |
2 |
10 |
3 |
| California |
1979 |
1 |
4 |
4 |
5 |
7 |
3 |
3 |
4 |
2 |
9 |
3 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
1980 |
1 |
4 |
4 |
5 |
5 |
2 |
4 |
3 |
3 |
10 |
4 |
| California |
1981 |
1 |
4 |
5 |
5 |
7 |
3 |
4 |
5 |
3 |
11 |
6 |
| California |
1982 |
1 |
3 |
4 |
5 |
10 |
4 |
3 |
9 |
3 |
11 |
5 |
| California |
1983 |
1 |
3 |
4 |
4 |
8 |
5 |
3 |
7 |
2 |
12 |
6 |
| California |
1984 |
1 |
6 |
4 |
7 |
6 |
9 |
2 |
8 |
4 |
11 |
5 |
| California |
1985 |
1 |
8 |
4 |
9 |
7 |
11 |
2 |
9 |
9 |
12 |
3 |
| California |
1986 |
1 |
7 |
3 |
8 |
6 |
11 |
3 |
4 |
9 |
13 |
4 |
| California |
1987 |
1 |
7 |
3 |
11 |
7 |
10 |
4 |
2 |
11 |
15 |
3 |
| California |
1988 |
1 |
6 |
3 |
8 |
9 |
12 |
4 |
2 |
10 |
12 |
1 |
| California |
1989 |
1 |
5 |
3 |
8 |
7 |
15 |
5 |
3 |
11 |
13 |
1 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
1990 |
1 |
6 |
3 |
9 |
5 |
19 |
4 |
3 |
11 |
19 |
2 |
| California |
1991 |
1 |
4 |
3 |
8 |
6 |
20 |
3 |
5 |
11 |
20 |
1 |
| California |
1992 |
1 |
4 |
3 |
8 |
4 |
23 |
3 |
6 |
7 |
21 |
1 |
| California |
1993 |
1 |
4 |
2 |
8 |
4 |
26 |
3 |
5 |
6 |
21 |
1 |
| California |
1994 |
1 |
8 |
3 |
12 |
5 |
29 |
4 |
5 |
10 |
24 |
2 |
| California |
1995 |
1 |
13 |
6 |
18 |
5 |
27 |
3 |
6 |
12 |
28 |
2 |
| California |
1996 |
1 |
19 |
6 |
22 |
9 |
27 |
4 |
7 |
19 |
35 |
2 |
| California |
1997 |
1 |
22 |
7 |
30 |
13 |
31 |
6 |
8 |
20 |
37 |
5 |
| California |
1998 |
1 |
27 |
9 |
32 |
19 |
33 |
7 |
10 |
24 |
38 |
6 |
| California |
1999 |
1 |
29 |
10 |
34 |
20 |
34 |
7 |
11 |
27 |
40 |
7 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
2000 |
1 |
30 |
9 |
33 |
16 |
31 |
7 |
9 |
26 |
40 |
7 |
| California |
2001 |
1 |
29 |
8 |
33 |
14 |
30 |
6 |
10 |
25 |
39 |
5 |
| California |
2002 |
1 |
27 |
10 |
28 |
12 |
31 |
6 |
10 |
25 |
38 |
5 |
| California |
2003 |
1 |
26 |
10 |
26 |
10 |
32 |
6 |
10 |
25 |
38 |
4 |
| California |
2004 |
1 |
24 |
12 |
25 |
9 |
37 |
6 |
16 |
22 |
35 |
3 |
| California |
2005 |
1 |
24 |
14 |
26 |
11 |
39 |
5 |
17 |
23 |
37 |
5 |
| California |
2006 |
1 |
26 |
14 |
29 |
12 |
41 |
4 |
18 |
23 |
37 |
4 |
| California |
2007 |
1 |
26 |
13 |
29 |
17 |
40 |
5 |
17 |
25 |
38 |
3 |
| California |
2008 |
1 |
26 |
14 |
25 |
16 |
41 |
5 |
19 |
25 |
40 |
3 |
| California |
2009 |
1 |
28 |
16 |
29 |
18 |
43 |
5 |
19 |
26 |
42 |
2 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
| California |
2010 |
1 |
28 |
17 |
30 |
20 |
41 |
5 |
19 |
25 |
39 |
1 |
| California |
2011 |
1 |
28 |
18 |
30 |
20 |
45 |
6 |
19 |
26 |
41 |
1 |
| California |
2012 |
1 |
25 |
16 |
24 |
18 |
45 |
5 |
19 |
24 |
38 |
1 |
| California |
2013 |
1 |
25 |
18 |
27 |
21 |
44 |
3 |
21 |
20 |
39 |
1 |
| California |
2014 |
1 |
27 |
17 |
29 |
23 |
40 |
4 |
21 |
23 |
38 |
2 |
| California |
2015 |
1 |
23 |
13 |
25 |
22 |
37 |
3 |
21 |
21 |
28 |
1 |
| State |
Year |
Population |
Index |
Violent |
Property |
Murder |
Rape |
Robbery |
Assault |
Burglary |
Larceny |
Vehicle |
|
| Since we developed
the table
of "California
Population
and
Rate of Crime
Rank Compared
to other States", we are interested in hearing how others are using the
table.
Email:
<EMAIL_ADDRESS>|
|
|
|
|
| *
United States * Alaska* Alabama
* Arkansas
*
Arizona
*
California
*
Colorado
*
Connecticut* Delaware
* Florida
*
Georgia
*
Hawaii
*
Iowa
*
Idaho
*
Illinois
*
Indiana
*
Kansas
*
Kentucky *
Louisiana
*
Massachusetts
*
Maryland *
Maine
*
Michigan
*
Minnesota
*
Missouri
*
Mississippi
*
Montana
*
North
Carolina *
North
Dakota *
Nebraska
*
New
Hampshire *
New
Jersey *
New
Mexico *
Nevada
*
New
York *
Ohio
*
Oklahoma
*
Oregon
*
Pennsylvania
*
Rhode
Island *
South
Carolina *
South
Dakota *
Tennessee
*
Texas
*
Utah
*
Virginia *
Vermont
*
Washington
D. C. *
Washington *
Wisconsin
*
West
Virginia *
Wyoming
* |
|


Highest score:
0 m : 0 s
Status
Moves :--
Time :--
0 : 0
--:Best of luck:--
Rules of game
Arrange the boxes such
that it comes in a order
of increasing numbers and
last box will be blank
More...
Challenge mode:


ExceptionConfiguration
.
passed
to a
created via XML Configuration - see mvc-configuration.xml
.
passed
to a switchable that may
or may not choose to handle the exception - see DemoExceptionConfiguration
. Switch state = on/off. Resolver is enabled
(disable)
disabled (enable)
The exceptions below show the use of a
SimpleMappingExceptionResolver
- the oldest way of handling exceptions in Spring dating back to V1
and still commonly used. It is equivalent to using a ControllerAdvice
- your choice.
To fully understand this demo, review the output in the server (console) log as you click the links below.
SimpleMappingExceptionResolver
.
SimpleMappingExceptionResolver
.
SimpleMappingExceptionResolver
.
Note how uninformative it is. Subclass
SimpleMappingExceptionResolver
and override its
doResolveException()
method to make additional context available in the model and in the
error page - see
ExampleSimpleMappingExceptionResolver
.
Demo 4: No Handler. For comparison, the same exceptions with no Simple Mapping Exception Resolver.
Demo 5: Working with Spring Boot.


Top
MockServer
Client
Expectations List
Index(adding order)
Request
Response
Times
Actions
Add Expectations
Request
Method
Any
GET
POST
PUT
HEAD
DELETE
CONNECT
OPTIONS
TRACE
PATCH
Path
Response
Response Type
Response
Forward
Callback
Error
Status Code
Reason Phrase
Delay
minutes
seconds
milliseconds
milliseconds
Body Type
Normal
UTF16/BINARY
Body
[{"name":"James","age":20},{"name":"Tom","age":16}]
Time
Times
Time to live
hours
minutes
seconds
milliseconds
minutes


Quiz Application
Select Quiz:
Asp.Net
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With CodeView, you get instant access to hundreds of anonymous mock interviews, available to those who are seeking to hone their technical skills. CodeView provides a place to discover potential and talent.
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-
What did you learn yesterday/this week?
The definition of the description list tag has changed from HTML4 to HTML5. Before it was interpreted as a definition list, now it is an association list with a name and associated value.
-
What excites or interests you about coding?
I am a creator and I am a writer. If coding is creating and writing, then coding is also a part of who I am.
-
What is a recent technical challenge you experienced and how did you solve it?
Eloquent JS:Chapter 4 exercise 3 was a real challenge for me. The exercise was to write a function that allows you to have a list and a number as arguments and return the returns the element according to the given position (number argument) in the list. I had to come up with a recursive function that would go deeper into the list to find the correct value.
-
What UI, Security, Performance, SEO, Maintainability or Technology considerations do you make while building a web application or site?
- UI:
- Mobile First: I try to take a progressive enhancement approach when designing and developing. I know this also helps with optimizing content and being mobile first.
- UX standpoint on UI- what is the main content/features that should be included across devices/platforms. I try keep the user personas, research, and design principles in mind throughout the entire process of building a site.
- Performance optimization:
- minify
- keep HTML small
- compress
- keep only relevant styles
- use media queries on link tag to unblock rendering
- Perferred Environment: Sublime
- Which version control systems are you familiar with?
Git
- Can you describe your workflow when you create a web page?
- Set up work environment-version control
- Set up file structure
- HTML markup-trying to keep HTML small
- Styling-trying to eliminate unnecessary repetitive styling
- Interactivity
- Review
-
Can you describe the difference between progressive enhancement and graceful degradation?
- Graceful Degradation: creates an user experience with modern browsers in mind and will create a limited lower experience for older browsers (possibly with less functionality)
- Progressive Enhancement: creates an user experience with functionality that will work across all browsers in mind and advanced functionality for browsers that can handle it
-
How would you optimize a website's assets/resources?
- Prioritize semantic HTML5 markup
- Keep assets light in size
- I use ImageOptim to optimize images.
- Load script after page has loaded
- Use media queries to split stylesheets so that irrelevant stylesheets won't block rendering
- Async JS so that JS files won't block rendering
- If CSS minimal, inline styling to optimize website performance
- Move scripts to the bottom
- Move CSS to the top
- Preload images
-
How many resources will a browser download from a given domain at a time?
- Usually, two requests downloading two files at a time
-
If you jumped on a project and they used tabs and you used spaces, what would you do?
I would follow the project norm because readability of code is important and the code should be easily understood by the most team members. I would change this setting in my text editor.
-
If you could master one technology this year, what would it be?
I would master JavaScript because it is used on every web browser and a lot of web technologies use JS. JS can be used for programming and to make a site highly interactive.


java.lang.Object | +--DGCharacter
This is the class that does most of the work in the Dagora Battle System. It's intended to be a superclass for any type of character needed.
| Field Summary | |
static short |
NUM_ABILITIES
Max number of abilities able to learn |
static short |
NUM_ATK
Maximum number of attacks possible for the character |
static short |
NUM_WEAPONS
Max number of weapons able to equip |
static double |
RUSH_RATIO
When HP/MaxHP is less than this, char enters Adrenaline mode |
static short |
XP_LV
Number of XP to rise to next level; current level is multiplied by this |
| Constructor Summary | |
DGCharacter()
Default constructor |
|
DGCharacter(java.lang.String name,
short level,
short hp,
short mp,
short maxhp,
short maxmp,
short atk,
short def,
short mag,
short spd,
short acc,
short luc,
short element)
Main constructor |
| Method Summary | |
short |
AccLv()
|
short |
AtkLv()
|
short |
DefLv()
|
short |
Element()
|
short |
GetAbility(short ind)
|
DGAttack |
GetAttack(short ind)
|
short |
GetStatus(short ind)
|
short |
HP()
|
boolean |
InRush()
|
short |
Level()
|
double |
Luck()
|
short |
MagLv()
|
short |
MaxHP()
|
short |
MaxMP()
|
short |
MP()
|
java.lang.String |
Name()
|
short |
NumAbilities()
|
short |
NumAttacks()
|
short |
NumStatus()
|
short |
NumWeapons()
|
short |
sampleMethod(short y)
An example of a method - replace this comment with your own |
short |
SpdLv()
|
DGAttack |
StandardAttack()
|
java.lang.String |
toString()
Gives the character's name |
| Methods inherited from class java.lang.Object |
|
| Field Detail |
public static final short NUM_ABILITIES
public static final short NUM_ATK
public static final short NUM_WEAPONS
public static final double RUSH_RATIO
public static final short XP_LV
| Constructor Detail |
public DGCharacter()
public DGCharacter(java.lang.String name, short level, short hp, short mp, short maxhp, short maxmp, short atk, short def, short mag, short spd, short acc, short luc, short element)
| Method Detail |
public short AccLv()
public short AtkLv()
public short DefLv()
public short Element()
public short GetAbility(short ind)
public DGAttack GetAttack(short ind)
public short GetStatus(short ind)
public short HP()
public boolean InRush()
public short Level()
public double Luck()
public short MagLv()
public short MaxHP()
public short MaxMP()
public short MP()
public java.lang.String Name()
public short NumAbilities()
public short NumAttacks()
public short NumStatus()
public short NumWeapons()
public short sampleMethod(short y)
y
- a sample parameter for a methodpublic short SpdLv()
public DGAttack StandardAttack()
public java.lang.String toString()
toString
in class java.lang.Object


--- date: 2011-09-02T00:00:00Z published: true title: Examining 5 oilsands claims by Daryl Hannah | CBC Debunking Environmental claims. url: /2011/09/02/examining-5-oilsands-claims-by-daryl-hannah-cbc-debunking-environmental-claims-/ ---
Actress and activist Darryl Hannah was arrested this week in Washington D.C during a protest against extending TransCanada's Keystone XL pipeline, which would carry oilsands crude from Alberta through six U.S. states to refineries in Texas.
On Wednesday, Hannah appeared on CBC's Power & Politics to debate Alykhan Velshi, founder of Ethicaloil.org, a website devoted to defending Canada's oil industry.
During the debate, Hannah made claims about the oilsands, some of which CBC News investigated:


Notebook name: hfir_reactor_element_analysis.ipynb
This notebook will use the output ASCII file created by the circular profile of a ring notebook and will display the peak of each element for each file, side by sided, to study any displacement of the elements.
If you need help accessing this notebook, check the How To > Start the python notebooks tutorial.
Check the full tutorial here
Select the profile ASCII file created by the circular_profile_of_a_ring notebook.
Check this tutorial if you need help using the file selection tool
The interface contains 2 tabs. The first tab Profiles, will allow you to select the threshold to use to isolate the peaks. The second tab Elements Position will display the position of each peak for each file loaded.
First thing to do is to visualize the profile of the tif file loaded (left table) and remove the rows that do not need to be part of the calculation, for example like the second row selected in the following screenshot.
A default threshold has been selected for the data set loaded. Make sure that threshold makes a clear center cut of every single data set to use.
For example, the following examples shows what kind of selection to avoid, and then a better one.
Once you are satisfy with the selection, click Calculate Elements Position. The program isolate all the local maximum, representing the center of each element (red symbols).
Make sure you browse through a random list of input data profile files to make sure you don’t see any out of position peaks, as shown in the following case.
In this case, increase the minimum number of points in a peak number in order to force the program to treat only the real peaks of at least n data points.
Once the elements positions have been calculated, clicking the Display ideal positions of n elements will show in the top plot the ideal positions of those n elements by using the full 360 degrees span starting at the first element found.
This tab displays the peak position of each element for each file. y-axis is the file index and x-axis is the angular position. Zoom in the top plot to really isolate each element and compare their angular position through the slices (projections).
2 tabs are provided at the bottom to quickly locate possible issues.
The first tab called all peaks found displays in red the element position that are outside of the tolerance range (defined at the bottom of the window).
The second tab called missing peaks considers the perfect table of number of files per ideal number of elements and shows in red the elements that have not been located in that particular “ideal” location.
One should starts worried when the red pattern repeats over multiple images!
Right click any of the cells of those 2 tables will display the angle value and the corresponding file name.
You can then just export the data into an ASCII data file by clicking the Export table data … button at the bottom right corner.


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- Release Date: April 4, 2014
- Starring: Anthony Mackie, Chris Evans, Cobie Smulders, Emily VanCamp, Frank Grillo, Hayley Atwell, Maximiliano Hernández, Robert Redford, Samuel L. Jackson, Scarlett Johansson, Sebastian Stan
- Genre(s): Action, Adventure, Sci-Fi, Thriller
- User Score: 8.8
- Runtime: 136 min
After the cataclysmic events in New York with The Avengers, Steve Rogers, aka Captain America, lives quietly in Washington, D.C. trying to adjust to the modern world. But when a S.H.I.E.L.D....


This release updates to Bootstrap v4.0.0-beta.2, for official release notes and ship list see: https://github.com/twbs/bootstrap/issues/23278
Replace the following files:
- Replace: assets/js/scripts.js and assets/js/scripts.min.js
- Replace: .navbar-toggleable-* classes with the new .navbar-expand-* classes
- Replace: index-event.html
- Replace: All index-onepager html files
- Replace: data-toggle=”unveil” with data-toggle=”blazy”
- Replace: index-jobs.html
- Replace: index-freealancer-portfolio.html carousel code
- Replace: Backup /assets/scss/custom then overwrite /assets/scss/ with new files, then add your /assets/scss/custom directory back, run gulp
- Replace: .header-transparent now has no default sticky styles, use the new .sticky-header-light, .sticky-header-dark, .sticky-header-primary classes to control the .header
- Removed: .header-translucent has been removed in favour of using .bg-* classes and .bg-op-(1-9) classes for more flexibility
- Replace: The following colour classes have been replaced:
- .bg-inverse (use .bg-dark)
- .bg-faded (use .bg-light)
- .text-inverse (use .text-dark)
- .border-faded (use .border-light)
- .divider-faded (use .divider-light)
- .hr-faded (use .hr-light)
- .navbar-inverse (use .navbar-dark)
- .btn-inverse (use .btn-dark)
- Replace: Sass variable changes:
- All colours have been added to a sass map called $as-theme-colours and $as-theme-colours-extended (with social brands)
- $extended-colour-options has been renamed to $as-theme-colours-extended-options and set to false as default.
- $theme-colours has been renamed to $as-theme-colours-default
- $theme-brand has been renamed to $as-theme-colours-brands
- $social_brands has been renamed to $as-theme-colours-social-brands
Changes:
- NEW: New magazine homepage variation with new header on sticky functionality and inline overlay search form
- NEW: Control scroll link speed by passing data-scroll-link-speed on any data-toggle=”scroll-link” link
- NEW: Added animated modals with animate.css (elements-modals.html)
- NEW: Responsive overlay menu header example (header-overlay-menu-responsive.html)
- NEW: Social media branding colours and utilities page (elements-social-media.html)
- NEW: Amazing header control with new sticky classes, restyle your header on sticky for the ultimate flexibility
- NEW: .nav-link-dd-effect main nav link class to make nav links act link dropdown links on hover
- UPDATED: Bootstrap beta 2 update (see: https://github.com/twbs/bootstrap/issues/23278)
- UPDATED: added new position classes .pos-BREAKPOINT-(y,x,a) for y axis, x axis and all sides and pos-BREAKPOINT-SIDE-auto for auto positioning
- UPDATED: Migrated from unveil to blazy for image lazy loading. Lazy load anything with an src!
- BUGFIX: Fixed markup issue on index-event.html
- BUGFIX: jobs sidebar sticky fix on small screens and header scroll issue fix
- BUGFIX: Tabs active background transparent fix
- BUGFIX: scroll menu active state on one pagers fix
- BUGFIX: IE11 carousel fix on index-freealancer-portfolio.html


Report: State Dept Quietly Released Ukraine Aid On Bolton’s Orders
Former National Security Adviser John Bolton reportedly directed the State Department to release $141 million in military to Ukraine several days before President Donald Trump claims he himself had authorized the aid’s release.
Bloomberg reported on Saturday that though Trump claims he had unfrozen funds for the aid on September 11 after Sen. Rob Portman (R-OH) had begged for him to do so that day, the State Department had already quietly released the aid on Bolton’s orders several days prior. Bloomberg could not verify whether Trump had signed off on Bolton’s directive.
The department reportedly released the aid after White House lawyers found that Trump and the Office of Management and Budget could not legally delay it, a finding the lawyers had made earlier in 2019. According to Bloomberg, the lawyers detailed the finding in a classified memo to Secretary of State Mike Pompeo.
An unnamed source told Bloomberg that White House Chief of Staff Mick Mulvaney was blindsided by Bolton’s order.
A spokesperson for the OMB told Bloomberg that Bolton lacked the authority to issue such an order and had not done so.


Tommy Dreamer gives his opinion on CM Punk’s role as an analyst on WWE Backstage
by Umid Kumar DeyFormer ECW Champion Tommy Dreamer thinks that CM Punk’s role as an analyst on WWE Backstage is great for the former WWE Champion, should he be ‘okay with his status’.
Punk became a member of the WWE Backstage team in a move that sent shockwaves around the wrestling world and has gotten the attention of almost everyone in the pro wrestling community.
Speaking about Punk on Busted Open Radio, Tommy Dreamer gave his views on his return and tried to assess the mindset of the former WWE Champion when he was denied a main event spot at WrestleMania 29, when he faced The Undertaker.
“Someone said no [about Punk being in the main event of WrestleMania 29]. And if that is what means something to you, then yes, at that point in his life, he’s cool. And he’s pi***d about that."
Dreamer also added that Punk “never has to see” Vince McMahon and Triple H and that his role as an analyst is great for him.
“If CM Punk is okay with his status, great. If Vince McMahon and Triple H are cool with CM Punk’s status – ‘cause I am sure they had to sign off on it.”
He then went on to bring up his own WrestleMania experience, citing it as some of the “worst days” of his career.
“At one point, my match was scratched to give more time to Melina and Ashley Massaro and I was like, ‘Are you kidding me?’ And then Rob Van Dam checked out while we were going to the ring ’cause there was no Rob Van Dam or Sabu shirts, and the show was in Michigan. He was quitting the WWE as we’re walking down the steps to come to the ring, and I’m like, ‘Rob, please. We have to go,’ because I’m trying to wrangle all these crazy people.”
We want to know what you think of the matches that you see on TV! Go to our WWE page here and rate and comment what you thought of them!


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A library is an important asset for any center of higher learning which provides services and facilities to meet the requirements of teaching & learning, research & development, and consultancy programs. Hence, efforts are always directed
towards
creating a very rich library having all important national and international journals/ newspapers, encyclopedia, and magazines, etc.
These libraries are a physical manifestation of our vision to distinguish the college as a center of resource in the knowledge era. The libraries aim to fulfill the crucial role of supporting learning and teaching activities to all members of
the
institute. As per AICTE norms, the central library of IERT has 4207 titles and 21204 volumes.


On behalf of Company Name
DD / MM / YY
New Zealand women's cricket captain, Amy Satterthwaite, has the world-famous Bradman Museum in Australia with a generous donation Of her match worn ODI uniform, from the recent series against Australia this summer.
Australia won the series, which makes Satterthwaite's gift all the more gracious and demonstrates the spirit of cricket.
Amy became the captain Of the New Zealand's 'VVhite Ferns' in October 2018. An all-rounder Of exceptional Skill, Amy has played 119 ODIs end 99 T20s for New Zeeland. In the 2016/17 season she became only the 2nd player, behind Kumar Sengakkare, to score four ODI centuries in a row.
Bradman Museum'S Executive Director, Rina said acknowledge generous donation as it is important to grow our contemporary artefacts within our collection. There has been a historical gap, and the museum's objective is to be a trusted source, central archive end authoritative resource for Australian cricket, end the history of women's cricket is a vital facet of the game's heritage."
To assist with this work, notable former Australian players, Lisa Sthalekar and Karen Hill (nee Price) are heading up a Women's Cricket Heritage project for the museum which includes Volunteers and former Australian and NSW players. One of the outcomes of this research will be to build a public display in time for the ICC T20 World Cup, held in Australia throughout February and March next year.
The Women's Cricket Heritage Project team. launched 2018, are currently undertaking the vital work of researching, cataloging, preserving end digitising the history Of women's cricket, meeting monthly at the Bradman Museum, in Bowral Australia.
The Executive Director added "The quality quantity Of women'S cricket related Objects has significantly over the past 3 years and we have catalogued in excess Of 1500 items in the past 12 months. The work to date been funded by the Bradman museum end recently the NSW government. We would welcome support, Volunteers or donations to assist with this vital work."
Amy uniform will be on display from April in the museum's International Cricket Hall Of Fame.
ENDS


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This site is obsolete and should be used for reference only. The information in this documentation is not guaranteed to work for Bot Framework SDK versions past 4.9.1.
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