Death Stranding is beautiful, confusing, and strange in all the right ways
Source: Android Central
Death Stranding was never going to live up to the mythologized version we created. You can count me as someone who finds review scores arbitrary and would rather focus on the text, and I find it especially difficult to assign a score to Kojima's latest project. It's already going down as one of the most divisive games out there. Time may change that, but whether for better or worse, is yet to be determined.
I'm not someone who thinks video games need to be fun to be good, but I also don't think they should be grueling and tedious. That's the best way to describe Death Stranding's early gameplay.  Death Stranding dishes out these moments sparingly, always knowing exactly when to do so.
At this point, I'm sure you've read countless other reviewers refer to it as a walking simulator and call Sam a UPS delivery man.
At a glance
Death Stranding
Bottom line: A captivating world and cast of characters are let down by tedious gameplay, but something about it just works and keeps your attention for the journey. If you like micromanagement, this is the game for you.
The Good
- Visuals are gorgeous
- Succeeds at creating tension when necessary
- Social aspects with other players
- Intriguing world and characters
- Great character performances
The Bad
- Gameplay is monotonous and tedious
- Too long for its own good
- Opening hours start off slow
- Doesn't fully capitalize on the premise its universe presented
- Makes my PS4 Pro fan sound like a jet engine frequently
Death Stranding Gameplay
Source: Android Central
I'm huddled under a rockface giving me a modicum of cover from the Timefall rain. I've been crouched there for what felt like an eternity, but was, in actuality, maybe 5 or 7 minutes. To put it into perspective, I had time to make myself a bowl of cereal and answer some emails on my phone. The rain hasn't stopped. I figured it would let up sooner rather than later because Death Stranding encourages you to take cover lest the rain damage your cargo. In the end, I begrudgingly left my pitiful shelter to carry on, not willing to wait any longer. I ended up ruining a good portion of my cargo containers.
Maybe this is the type of reaction the game hoped to create. Reconnecting the world is a whole lot harder than tearing it apart. Sacrifices need to be made, and it's a lonely job. But it's seldom thankless. No matter where I went, the people expressed their gratitude, whether in the form of a simple thank you or something more tangible, like gear upgrades.
Source: Android Central
The world feels tremendously intimidating on foot, but definitely less so on a motorcycle. I found that my motorcycle became a crutch, and I felt lost whenever I needed to abandon it to treck past BTs so the noise wouldn't alert them. Out of everything in the game, I think this is the most useful tool, second only to actual bridges and zip lines that players build to help you cross difficult terrain.
Violence is discouraged for a very good reason.
When it comes to combat, violence is discouraged for a very good reason: dead bodies necrotize and cause a void out, which you can think of as a nuclear bomb going off. Because of this, you'll find yourself either stealthily sneaking through enemy territory or avoiding conflict altogether. If that's not enough, you have other tools at your disposal to incapacitate enemies.
BTs, those otherworldly creatures brought about by the Death Stranding phenomenon in the game, are a different story. Sam's blood and bodily fluids harm them, so whenever you use your private quarters, you'll receive grenades that do some damage.
Reconnecting the world is a whole lot harder than tearing it apart.
MULEs, rogue porters addicted to cargo, are decidedly less threatening most of the time. I could be surrounded by a group of them and easily take a minute or two to knock all of them out. They don't pose much of a threat, making them just an annoyance. And when you do encounter tougher MULEs who use lethal weapons, they're still annoying and not fun to fight.
Some sections act like your typical action moments with third-person shooting, and they're not good.  The gunplay is awkward at best.
What is easily my favorite aspect of the game are its social aspects. If you happen to lose cargo, other players can find it in their world and deliver it for you. Players can set up timefall shelters, ziplines, bridges, ladders, safe houses, warning signs — you name it. I used these countless times throughout Death Stranding, and each time I used one, I couldn't help but think how grateful I was that it was possible. Despite being a single-player game, you never really feel alone, in a sense. It's almost like a shared-world experience.
Death Stranding Story and characters
Source: Android Central
The world of Death Stranding is incredibly interesting, and when you take the time to listen to some of the exposition, the premise is easy to follow. When it comes to specifics about understanding the world, that's when it gets harder to parse. It's never so confusing that you feel completely lost. If someone asked me to explain what it's about, I think I could do so fairly well in simple terms. And if you want a deeper understanding of its lore, Death Stranding offers a lot of information through written interviews.
As interesting as some of the characters were, I didn't really connect with them on an emotional level. Several of them have tragic backstories, particularly Mama and Fragile, that players can sympathize with, they just didn't really do it for me. But some of the unexpected twists involving a few characters hit their mark and make you question everything you thought you knew.
Death Stranding Visuals and performance
Source: Android Central
Walking through Death Stranding felt like taking a trip through an untouched, scenic landscape from a painting. The world is practically empty, and that strengthens its atmosphere. You'll travel through beautiful mountains and valleys, snow-covered hills, rocky, desolate terrain, and the ruins of former cities. Maybe it's the color palette and how empty the world is, but I've seen games with more diverse environments despite everything I just listed. Regardless, the world is enthralling.
In terms of performance, overall, the game ran well aside from some slowdowns in its menus. It did make my PS4 Pro fan sound like an airplane taking off, but it didn't affect the gameplay in any way.
Interpret Death Stranding how you want
Source: Android Central
You can say only "intellectuals" will get Death Stranding all you want. That doesn't make it true. It does have a broader message and succeeds on some level at conveying it thoughtfully. Kojima wants to bring the world together; connect everyone when it's more divided than ever. Other times, it's just a game where you fashion ammunition out of your own blood and human waste. I'm not about to debate whatever deep metaphors you believe Kojima had in mind. It boils down to a heavy-handed way of saying "build bridges, not walls."
It boils down to a heavy-handed way of saying "build bridges, not walls."
There are times when talking about the game feels like arguing with a high school English teacher who insists that there's symbolism in everything in a book, even if the author didn't intend it. Twitter is guilty of this as well, with a popular thread about the animated movie Ratatouille insisting that Remy cooked for the food critics' mother years prior, and that's why Remy's food reminded Ego of his mother's. As it turns out, this was a case of Pixar reusing assets for the film. No hidden meaning or anything so clever. Now imagine what'll happen when people start replacing Ratatouille with Death Stranding in similar scenarios.
At the end of the day, you take away from Death Stranding what you do. If you find specific symbolism in it, more power to you. Death of the author exists for a reason, contentious as it may be. It's your prerogative if you want to play through dozens of hours of gameplay tripping over rocks and carefully balancing cargo because the game speaks to you on some level.
Bottom line
Source: Android Central
Is Death Stranding boring? Yes. Is it exhilarating? Yes. Is it confusing and interesting and bizarre and thoughtful? Also yes. It's not something to be put in a box and defined as one thing. A lot of people won't make it past the opening hours. Some won't be able to get enough of it. This is true for most games, but especially this one. I'm not sure if I can tell you I had fun with it myself. What I can say is that the character performances are top-notch, the visuals are stunning, and the world of Death Stranding is fascinating.
3.5 out of 5
For all of its faults — and there are numerous — I'm glad that Death Stranding exists. I want developers to be able to pitch and create games like this. I want publishers to fund them. Maybe some ideas stick, maybe they don't. But the industry will never grow as a medium if we keep recycling old ideas. Make weird games.
Reconnect
Death Stranding
Build bridges at your own peril
A captivating world and cast of characters are let down by tedious gameplay, but something about it just works and keeps your attention for the journey. If you like micromanagement, this is the game for you.
We may earn a commission for purchases using our links. Learn more.


This setting controls how agent pods are retained after the Jenkins build completes. The following retention policies are provided:
Note: Kubernetes administrators are responsible for managing any kept agent pod. These will not be deleted by the Jenkins Kubernetes plugin.


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No.
The change part can be complicated, if you want. It is best to keep it small and understandable. Here is almost the same loop as in the previous example, but now the control variable is incremented by two.
Recall (from Chapter 39) that count += 2
count
.
Try to predict the output before you run the program.
int count; for ( count = 0; count < 7; count += 2 ) { System.out.println( "count is: " + count ); } System.out.println( "\nDone with the loop.\nCount is now" + count);
Here is a JavaScript version of this loop.
The change to count
is done at the bottom
of the loop body.
This means that the last value that count
gets is the first one that
fails the test count < 7
.
Questions like this are common on midterm and final examinations. If you rush, you are likely to get it wrong. But with careful thought they are easy enough.


A primary goal of a Montessori program is to help each child reach full potential in all areas of life. The Montessori curriculum, under the direction of a specially educated teacher, provides the resources and atmosphere for exploration and discovery, allows students to experience the joy of learning, promotes the development of self-esteem, and fosters respect for one’s self, for others and for the environment.
In order for self-directed learning to take place, the whole learning environment – room, materials, and social climate – must be supportive of the learner. The teacher provides necessary resources, including opportunities for children to function in a safe and positive climate. The teacher thus gains the children’s trust, which enables them to try new things and to build self-confidence.
Dr. Maria Montessori’s observations of the kinds of activities that children enjoy and go back to repeatedly led her to design a number of multisensory, sequential, and self correcting materials that facilitate the learning of skills and lead to learning of abstract ideas.
The Montessori teacher functions as a facilitator of learning. As such, he or she is a designer of the environment, resource person, guide, role model, demonstrator, and meticulous observer and recorder of each student’s behaviour and growth.
Each Montessori classroom operates on the principle of freedom within limits. Every class has a set of ground rules based on respect for each other and the environment.
Children are free to work at their own pace with materials they have chosen, either alone or with others. The teacher relies on observations of the children to determine which new activities and lessons to introduce to an individual child, small group, or to the whole class. The aim is to encourage active, self-directed learning and to strike a balance of individual mastery within small group collaboration with the whole group community.
The multi-year age span in each class provides a family like grouping where learning can take place naturally. More experienced children share what they have learned while reinforcing their own learning through teaching. This peer group learning/teaching is intrinsic to Montessori.
Dr. Maria Montessori, as one of the first female physicians to graduate from the University of Rome, she was invited to open a child-care center for poor families in the slums of Rome.
She called the center, Casa dei Bambini (Children’s House) and based her program on her observations that children learn best in a homelike setting, filled with developmentally appropriate materials that provide experiences to facilitate the growth of self motivated, independent learners.
“The First Time” by Beth Black, Montessori Life. Spring 2011
American Montessori Society
The Montessori Foundation
Parent Child Press
The Montessori Method: Succeeding at their own pace
Maria Montessori Google Doodle: How Montessori Education ‘Programmed’ Google’s Founders


As part of the preparations for the workshop on container security that myself and Iain Smart ran at this year’s Steelcon, there were some concerns that our standard option of SSH access might be blocked by the venue’s Wi-Fi, so a backup plan was in order. As a result, we were looking into how to provide a browser based terminal for students running on a host in AWS EC2.
After looking at various options, we decided to see if we could get code-server working. It’s a really nice project that provides a hosted version of VS Code, which gives you file exploring/editing, a terminal and port forwarding for local applications, all in a browser.
After the conference, I decided to extract the config into a stand-alone set of Ansible tasks, and put it into my Container Security Workstation repo. You can see the overall playbook here but we’ll go through the key parts in this post.
There were a couple of interesting technical aspects to getting it all working, which I thought I’d write-up here, in case it’s of use to other people!
Setting up an EC2 instance with Ansible
This is relatively straight-forward, but with one caveat, that Ansible changed the syntax of this, so if you have an older version of ansible this may not work. First up install the AWS ansible galaxy role ansible-galaxy collection install amazon.aws
then have a block like this to setup the EC2 and wait for it to be available
- name: start an instance with a public IP address
amazon.aws.ec2_instance:
name: "Container Security Workstation"
key_name: "{{ key_pair }}"
vpc_subnet_id: "{{ subnet_id }}"
region: "{{ region }}"
instance_type: "{{ instance_type }}"
security_group: "{{ security_group }}"
network:
assign_public_ip: true
image_id: "{{ ami_id }}"
wait_timeout: 60
state: running
register: ec2
- name: Add all instance public IPs to host group
add_host: hostname={{ item.public_ip_address }} groups=ec2host
loop: "{{ ec2.instances }}"
- name: Wait for SSH to be available
delegate_to: "{{ item.public_ip_address }}"
wait_for_connection:
delay: 20
timeout: 60
with_items: "{{ ec2.instances }}"
A couple of key points to note for this. you’ll need a valid API connection to AWS with enough rights to create an EC2 instance. You’ll then need to have the information for the various variables here
key_pair
- The name of an SSH key pair in your AWS account to use for access to the hostsubnet_id
- The ID of the subnet to place the EC2 inregion
- The region to useinstance_type
- The EC2 instance type to usesecurity_group
- A security group that exists in your AWS account which allows at least 22/TCP, probably also 443/TCP for Caddy.ami_id
- the AMI ID to use for the host. In my case I use an ubuntu:22.04 based AMI.
Code Server Install and Config
The basic installation of Code server is pretty straight-forward. They provide a deb package, so we can just download and install that :-
- name: Install Code Server
get_url:
url: https://github.com/coder/code-server/releases/download/v4.14.0/code-server_4.14.0_amd64.deb
dest: /tmp/code-server.deb
- name: Install Code Server
apt:
deb: /tmp/code-server.deb
state: present
- name: start code server
systemd:
name: code-server@ubuntu
state: started
enabled: yes
After that there’s a couple of configuration changes to make, first I wanted to move the port from 8080
to 18080
as I’ll often use 8080
for other things. Using Ansible’s lineinfile
was a good way to do that, and pointing it at the default config file location in the user’s home directory. As we’re using an Ubuntu EC2 instance here, that’ll be /home/ubuntu
.
- name: Change port to 18080 in the code-server config file
lineinfile:
path: /home/ubuntu/.config/code-server/config.yaml
regexp: '^bind-addr:'
line: 'bind-addr: <IP_ADDRESS>:18080'
You might notice that we’re still listening on <IP_ADDRESS>
here, but we’ll get to making this accessible remotely in a bit!
Next up we want to change the password from the default value, of course. Code Server has different authentication options available, but for this purpose, a static password (assuming it’s suitably strong) should be fine.
- name: Change password to the value of code_password in the code-server config file
lineinfile:
path: /home/ubuntu/.config/code-server/config.yaml
regexp: '^password:'
line: 'password: {{code_password}}'
This section just sets the password to whatever is held for the ansible var code_password
.
Lastly for this section, we want to re-start the server, so our configuration changes take effect.
- name: restart code server
systemd:
name: code-server@ubuntu
state: restarted
enabled: yes
Making it available remotely - Caddy!
I’ve mentioned before about how cool Caddy is for a variety of reasons, and we can make use of it here to expose the Code server over TLS. As a pre-requisite, this section uses an Ansible galaxy role, which can be installed with ansible-galaxy collection install maxhoesel.caddy
. If you’re happy enough with SSH port-forwarding, that would be another option here.
We want to change the configuration of Caddy so that it’ll provide a reverse proxy from <IP_ADDRESS>:18080
to <IP_ADDRESS>:443
and set-up TLS
- name: Setup the Caddy server
include_role:
name: maxhoesel.caddy.caddy_server
vars:
caddy_config_mode: "Caddyfile"
caddy_caddyfile: |
:443 {
tls internal {
on_demand
}
reverse_proxy :18080
}
- name: Restart Caddy
systemd:
name: caddy
state: restarted
enabled: yes
Extra Credit - Valid TLS cert
At this point you’ve got a configuration that’ll work, but the certificate won’t be trusted by the browser, which isn’t ideal (if only that you’ll need to click through a warning when you get to it). We can get TLS certificates issued on demand using Lets encrypt and Caddy which has a very neat trick of provisioning these on the fly, we just need a valid DNS record for the host.
Of course you could do this manually after set-up, but it’d be neat to have it provisioned automatically. Here we just need a DNS provider that’s got an API and, ideally, Ansible integration. Fortunately the DNS provider I use DNSimple has both of these things!
First up we need to register our DNS record. This process will vary depending on your provider but the general concepts will likely remain. You’ll need an API key for the provider and a domain to host at. The task below sets up a host called csw
and a domain specified as the var dns_domain
and sets the A
record to point to inventory_hostname
which should be the external IP address of the EC2 we’ve started.
- name: Authenticate to DNSimple & Create Record
community.general.dnsimple:
account_email: "{{ dnsimple_account_email }}"
account_api_token: "{{ dnsimple_api_key }}"
domain: "{{ dns_domain }}"
type: A
# Change as needed
record: csw
solo: yes
ttl: 360
value: "{{inventory_hostname}}"
state: present
delegate_to: localhost
register: dns_record
Now we’ve got a valid DNS record, we just need to re-write the Caddyfile that we’re using so that Caddy will provision a cert for us on access. These tasks remove the old Caddyfile, add a new one specifying our host and domain and setting up TLS and then re-start Caddy.
- name: remove old Caddy Config
file:
path: /etc/caddy/Caddyfile
state: absent
- name: Add caddyfile contents
copy:
dest: /etc/caddy/Caddyfile
content: |
csw.{{dns_domain}}:443 {
tls {{dnsimple_account_email}}
reverse_proxy :18080
}
- name: Restart Caddy
systemd:
name: caddy
state: restarted
enabled: yes
If all works, once you’ve run your playbook, it should look something like this :-
Conclusion
At the end of all this you should have a nice Web hosted development/testing environment with a terminal and port-forwarding, which could be handy for a number of reasons.


ÁVision Build Log
Tool Versions:
IDE-Version: Ž╠Vision V<IP_ADDRESS> Copyright (C) 2015 ARM Ltd and ARM Germany GmbH. All rights reserved. License Information: ti 20607, rfgyuhj, LIC=EPP8T-R3S85-9X4UW-Q58UG-13VTV-UCM1K Tool Versions: Toolchain: MDK-ARM Professional Version: <IP_ADDRESS> Toolchain Path: D:\Keil_v5\ARM\ARMCC\Bin C Compiler: Armcc.exe V5.05 update 1 (build 106) Assembler: Armasm.exe V5.05 update 1 (build 106) Linker/Locator: ArmLink.exe V5.05 update 1 (build 106) Library Manager: ArmAr.exe V5.05 update 1 (build 106) Hex Converter: FromElf.exe V5.05 update 1 (build 106) CPU DLL: SARMCM3.DLL V<IP_ADDRESS> Dialog DLL: DCM.DLL V<IP_ADDRESS> Target DLL: lmidk-agdi.dll V??? Dialog DLL: TCM.DLL V<IP_ADDRESS>Project:
G:\Keil\TI TM4C\IIC_6050\Demo.uvprojx Project File Date: 08/12/2018Output:
Build target 'Target 1' compiling mpu6050.c... drivers\MPU6050\mpu6050.c(250): warning: #177-D: variable "x1" was declared but never referenced static float x,y,z,x1,y1,z1; drivers\MPU6050\mpu6050.c(250): warning: #177-D: variable "y1" was declared but never referenced static float x,y,z,x1,y1,z1; drivers\MPU6050\mpu6050.c(250): warning: #177-D: variable "z1" was declared but never referenced static float x,y,z,x1,y1,z1; drivers\MPU6050\mpu6050.c(251): warning: #177-D: variable "File_Buf" was declared but never referenced static float File_Buf[3][20]; drivers\MPU6050\mpu6050.c(252): warning: #177-D: variable "num" was declared but never referenced static int num = 0; drivers\MPU6050\mpu6050.c(253): warning: #177-D: variable "i" was declared but never referenced int i=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum1" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum2" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum3" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum4" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum5" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c(255): warning: #177-D: variable "sum6" was declared but never referenced long sum1=0,sum2=0,sum3=0,sum4=0,sum5=0,sum6=0; drivers\MPU6050\mpu6050.c: 12 warnings, 0 errors compiling timer_config.c... linking... Program Size: Code=15956 RO-data=3728 RW-data=1644 ZI-data=1100 ".\Objects\Demo.axf" - 0 Error(s), 12 Warning(s).Collection of Component include folders:
G:\Keil\TI TM4C\IIC_6050\RTE D:\Keil_v5\ARM\PACK\Keil\TM4C_DFP\1.1.0Collection of Component Files used:


--- theme : name : twitter layout: default --- {% include JB/setup %} {% include themes/twitter/post.html %}


Source code for dicealyzer.transformer
"""
This module contains the transformer class, that turns a given parse tree into a dice pool.
"""
from operator import add, sub, mul, truediv
from lark.visitors import Transformer, v_args
from .pools import BinaryOperatorPool, DiceTypePool, DiceDropPool, ConstPool
[docs]def keep_sum(rolls, keep, lowest=False):
"A small helper function for keeping certain dice rolls"
rolls.sort(reverse=lowest)
return sum(rolls[len(rolls) - keep:])
[docs]@v_args(inline=True)
class DicePoolTransformer(Transformer):
"Transforms a read statement into a dice pool that can be rolled multiple times."
# pylint: disable=no-self-use, invalid-name
[docs] def constpool(self, n):
"The constpool rule only read NumberPools, so return one of those."
return ConstPool(n)
[docs] def dice(self, *values):
"This rule reads in the number of dice and their sides. Returns them as a tuple of ints."
try:
amount, sides = values
except ValueError:
amount = 1
sides = values[0]
return (amount, sides)
[docs] def drop_lowest(self, dice):
"Reads in a dice pool that drops the lowest dice."
return DiceDropPool(dice[1], dice[0], 1, False)
[docs] def drop_highest(self, dice):
"Reads in a dice pool that drops the highest dice."
return DiceDropPool(dice[1], dice[0], 1, True)
[docs] def keep_highest(self, dice, keep):
"Reads in a dice pool that keeps the `keep` highest."
return DiceDropPool(dice[1], dice[0], keep, False)
[docs] def keep_lowest(self, dice, keep):
"Reads in a dice pool that drops the `keep` lowest."
return DiceDropPool(dice[1], dice[0], keep, True)
[docs] def add(self, p1, p2):
"Combined several dice pools by adding them up."
return BinaryOperatorPool(add, '+', p1, p2).simplify()
[docs] def sub(self, p1, p2):
"Combined several dice pools by subtracting them."
return BinaryOperatorPool(sub, '-', p1, p2).simplify()
[docs] def mul(self, p1, p2):
"Combined several dice pools by multipolying them."
return BinaryOperatorPool(mul, '*', p1, p2).simplify()
[docs] def div(self, p1, p2):
"Combined several dice pools by dividing them."
return BinaryOperatorPool(truediv, '/', p1, p2).simplify()
[docs] def start(self, *n):
"This grammar only returns one object, which is a dice pool."
try:
return n[0]
except IndexError:
return None


//favicon //
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Fly Bee Fly app is made by Goodmorninglabs
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Last Edited on January 12th, 2017


The Team Generator
A Random Student
Neighboring Pair
Neighboring Three
Random Pair
Random Three
Random N-pairing


Created by the 'A-team' of the Causeway CoderDojo, using all the web skills they have learned!
Plenty of things to see around the coast
Lots of events to suit every family needs
Lots of places to see around and outside the town


| Package | Description |
|---|---|
| org.apache.pig |
Public interfaces and classes for Pig.
|
| org.apache.pig.backend.hadoop.accumulo | |
| org.apache.pig.backend.hadoop.executionengine.mapReduceLayer | |
| org.apache.pig.backend.hadoop.hbase | |
| org.apache.pig.builtin |
This package contains builtin Pig UDFs.
|
| org.apache.pig.builtin.mock | |
| org.apache.pig.impl.builtin | |
| org.apache.pig.impl.io | |
| org.apache.pig.impl.streaming | |
| org.apache.pig.impl.util | |
| org.apache.pig.piggybank.storage |
| Modifier and Type | Interface and Description |
|---|---|
interface |
LoadStoreCaster
This is just a union interface of LoadCaster and StoreCaster,
made available for simplicity.
|
interface |
StoreCaster
An interface that provides methods for converting Pig internal types to byte[].
|
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
StreamToPig.getLoadCaster()
Deprecated.
This will be called on both the front end and the back
end during execution.
|
abstract LoadCaster |
PigStreamingBase.getLoadCaster()
This will be called on the front end during planning and not on the back
end during execution.
|
LoadCaster |
LoadFuncWrapper.getLoadCaster() |
LoadCaster |
LoadFunc.getLoadCaster()
This will be called on both the front end and the back
end during execution.
|
LoadCaster |
EvalFunc.getLoadCaster()
This will be called on both the front end and the back
end during execution.
|
| Modifier and Type | Class and Description |
|---|---|
class |
AccumuloBinaryConverter
A LoadStoreCaster implementation which stores most type implementations as
bytes generated from the toString representation with a UTF8 Charset.
|
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
AbstractAccumuloStorage.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
MergeJoinIndexer.getLoadCaster() |
| Modifier and Type | Class and Description |
|---|---|
class |
HBaseBinaryConverter |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
HBaseStorage.getLoadCaster()
Set up the caster to use for reading values out of, and writing to, HBase.
|
| Modifier and Type | Class and Description |
|---|---|
class |
TextLoader
This load function simply creates a tuple for each line of text that has a
single chararray field that
contains the line of text.
|
class |
Utf8StorageConverter
This abstract class provides standard conversions between utf8 encoded data
and pig data types.
|
| Modifier and Type | Field and Description |
|---|---|
protected LoadCaster |
PigStorage.caster |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
TextLoader.getLoadCaster() |
LoadCaster |
PigStreaming.getLoadCaster() |
LoadCaster |
JsonLoader.getLoadCaster() |
LoadCaster |
BinStorage.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
Storage.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
SampleLoader.getLoadCaster() |
LoadCaster |
DefaultIndexableLoader.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
ReadToEndLoader.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
PigStreamingUDF.getLoadCaster() |
| Modifier and Type | Method and Description |
|---|---|
static Object |
CastUtils.convertToType(LoadCaster caster,
byte[] bytes,
ResourceSchema.ResourceFieldSchema fieldSchema,
byte dataType) |
| Modifier and Type | Method and Description |
|---|---|
LoadCaster |
AllLoader.getLoadCaster() |
Copyright © 2007-2017 The Apache Software Foundation


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Bright tin finish; Securely riveted steel handles; Cans nest closely for storage; 14" H x 12-1/2"OD, holds 6-1/2 gallons (50 lbs); Made in USA. Note: Our many years of shipping this . Anonymous User on 5/18/2011, said: Love my tins. I ordered a .
http://www.lehmans.com/p-3100-tin-lard-cans.aspx -
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438 Products . Tin Can Gallon, Source Tin Can Gallon Products at Cans, Packaging Boxes from . 5 Gallon Conical Tin Can Automatic Sealing Machinery. Min.
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BT Sport retains Champions League rights in blockbuster deal
by BeSoccerBT Sport has retained exclusive UK broadcast rights to UEFA's European club competitions after agreeing a reported 1.2 billion ($1.5 billion) deal on Friday.
The deal, which is said to have cost £400 million per year and commences in 2021, gives BT the rights to all 420 games from the Champions League, Europa League and the new Europa Conference League for a further three seasons.
The broadcaster has held the rights since 2015 after it agreed an £897 million deal with European football's governing body.
BT Sport, owned by Britain's biggest broadband and mobile operator BT, is a paid subscription service.
Featured links
- Ballon d'Or winner already been decided
- The latest football transfers news and rumours
- Possible 'Clasico' in the Spanish Supercup final
- Who has the most free-kicks goals: Messi or Cristiano?
There will be an increase of 77 games from the previous deal, which includes highlights and in-match clips.
BT Sport reported a 26 per cent increase in Champions League viewer hours, with last season's final between Liverpool and Tottenham made available for everyone to watch in the UK via social media.
A record 11.3 million people tuned in across all platforms to watch Liverpool win 2-0 as they lifted the European Cup for a sixth time in Madrid.
Chief executive of BT's Consumer Division Marc Allera said: "We're delighted to remain the home of UEFA Champions League in the UK."


This is a workshop created for the IGGI Symposium of 2018. As the workshop is quite brief, we will try and cover a broad range of seaborn and dataviz with 15 minutes per excercise.
These annotations are here to supplement the talk (and vise versa) as we work along, and in case you may want to revisit the material later. Don't feel that you have to read as we talk, they are simply here to assist you.
A note on the programming quality of this workshop: our focus here was to present seaborn in the quickest-to-learn manner for a time constrained workshop. As such, some styling here may be particularly offensive (as well as some hardcoding). For this, we do apologise in advance, but hopefully you can find something useful from this.
A final warning before we continue, some of the later cells in this notebook can be a bit computationally intensive for older or laptop hardware, so we advise against running all cells unless you accept some non-insignificant load.
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import make_classification
from sklearn.preprocessing import Normalizer
from sklearn.model_selection import StratifiedShuffleSplit, GridSearchCV
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
from sklearn.neighbors import KNeighborsClassifier as KNN
from sklearn.linear_model import LogisticRegression as LR
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.neural_network import MLPClassifier as MLPC
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from toolbox.tools import *
import statsmodels.api as sm
df_gm = pd.read_csv('data\\dataframe.csv')
df_ml = pd.read_csv('data\\dataframe_ml.csv')
df_ts = generate_time_series(len_series=100, n_conditions=2, n_individuals=100)
df_ln = generate_linear(n_individuals=30, n_conditions=2)
df_pl = generate_poly(n_individuals=30, n_conditions=2, order = 3)
For the start of this workshop, we will work briefly with a basic dataset of NBA player statistics from the season of 2017-2018 provided by Basketball Reference.
nba_df = pd.read_csv('data\\nba.csv')
While this is not a workshop on Pandas, we will go over some details of the package when they are relevant to the task at hand. For those unfamiliar with Pandas, it is a data wrangilng library that is particularly effective at manipulating sets of data. At lower levels, Pandas series are effectively built upon numpy arrays.
It's always a good idea to start by taking a cursory glance at your dataframe, to see what's there and to make sure the data is structured in a manner that you desire. You can peek at (and pull data from) the head and tail of your dataframe using the head()
and tail()
methods.
nba_df.head()
For a quick table of summary statistics, dataframes have a describe()
method that quickly pulls some descriptive statistics of our data distributions.
nba_df.describe()
We will now begin with some descriptive visualisations to explore this data. Some examples here are not the best of exploratory analyses, but the focus here is the visualistion.
For this, we use seaborn, which is used in conjunction with (and is in fact, a higher level API for) Matplotlib in order to generate some very quick and very attractive graphs. Let's start with simple boxplots. However, if we just use the boxplot()
method on all our variables, the figure will be incomprehensible. So, we will use .loc
to access the columns of interest.
boxplot_df = nba_df.loc[: ,['PS/G', 'AST', 'TRB', '3PA', 'FGA']]
sns.boxplot(data=boxplot_df)
plt.show()
Notice that PS/G (Points per Game) is causing somewhat of an imbalance in this figure, so we can opt to recreate the same plot with something more comparable. Let's compare the 2 closest variables, Total Rebounds and 3 Point Attempts.
boxplot_df = boxplot_df.drop(['PS/G', 'AST', 'FGA'], axis=1)
sns.boxplot(data=boxplot_df)
plt.show()
The boxplot is a very nice visualisation of the median, quartiles, and potential outliers of your data. However, in many cases the violin plot is a more desirable visualisation, especially in cases where your distribution might be bimodal or multimodal. Check Miao Yu's github page on violin plots for some quick literature on this.
sns.set(font_scale=1, style = 'darkgrid', context='notebook', rc={"figure.figsize": (12, 5)})
#Generate smaller dataframe for violin plot
violinplot_df = nba_df.loc[: ,['3P%', '2P%', 'FG%']]
sns.violinplot(data=violinplot_df)
plt.show()
Now let's try to take a closer look at our data. In the NBA, players play one of five positions (at least in name) and we could draw a simple count plot (also known as a bar graph) of this.
Before we continue, notice in our dataframe originally that 'Pos' is currently a string, so we will need to work with a new column that will allow us to enumerate these.
nba_df['Position'] = nba_df['Pos']
nba_df['Pos'] = nba_df.Pos.astype('category').cat.codes.astype(np.int64)
nba_df.fillna(0)
nba_df.head()
Now let's look at the frequencies of players in each position and draw the plot.
nba_df['Position'].value_counts()
sns.set(font_scale=1, style = 'darkgrid', context='notebook', rc={"figure.figsize": (12, 5)})
# Create an order variable to sort by descending
pos_desc = nba_df['Position'].value_counts().sort_values(ascending=False).index
pos_countplot = sns.countplot(x='Position',
data=nba_df,
order=pos_desc,
palette="colorblind")
plt.title('Position Counts')
plt.xlabel('Position')
plt.ylabel('Count')
plt.show()
It seems there are some players who are labelled differently in the data. We will deal with this later on when we return to the positions variable.
For now, let's take a look at distribution plots of some offense data. We'll examine Points per Game (PS/G) and 3 Point Shots per Game (3P).
In seaborn, histograms and continuous probability distributions (Kernel Density Estimations) are combined into a single plot type. You can amend the parameters of distplot()
to remove one or the other.
### sns.set(font_scale=1, style = 'darkgrid', context='notebook')
psg_distplot = sns.distplot(nba_df['PS/G'],
label='Points per Game')
plt.title('Points per Game Distributions')
plt.xlabel('Points per Game')
plt.ylabel('Density')
plt.show()
p3_distplot = sns.distplot(nba_df['3P'],
label='3 Points per Game',
kde=False,
rug=True)
plt.title('3 Points per Game Distributions')
plt.xlabel('3 Points per Game')
plt.ylabel('Density')
plt.show()
We can also carry out some caveman correlations here by generating a correlation matrix of (nearly) all of our variables. I removed some variables here to make the figure slightly easier to read.
The heatmap()
method is generally flexible for many matrices, so we recommend you to play around and think about how you might use this elsewhere. Later on, we'll revisit this figure in the machine learning analysis of the workshop.
corrmat_df = nba_df.drop(['Rk', 'Player', 'Pos', 'Tm', 'Age',
'2P%', '3P%', 'FG%', 'FT%', 'eFG%'],
axis=1)
corrmat = corrmat_df.corr()
sns.heatmap(corrmat)
plt.show()
As a core principle of visualisation, remember that you are communicating data and research with these tools. Depending on your audience, you want to minimise the complexity of certain visualisations like matrices in order to effectively present your data.
Let's turn to the scatterplot for a moment. Here, we'll plot the assists players are making against their turnovers. It's conceivable that if you're making a lot of passes, you'll also miss occassionally and end up turning the ball over to the other team.
scoring_scatterplot = sns.scatterplot(x='AST',
y='TOV',
hue='Position',
palette='Set1',
data=nba_df)
plt.title('Assists vs Turnovers')
plt.xlabel('Assists')
plt.ylabel('Turnovers')
plt.show()
Because this is professional sports, we can find outliers in many directions. In the second plot, we increase the size of the graph in order to visualise this difference even further. If you are presenting data to people and you wish to (honestly!) emphasise your data in such a way, this is a valuable manipulation in your toolbox.
sns.set(font_scale=1, style = 'darkgrid', context='notebook', rc={"figure.figsize": (12, 8)})
scoring_scatterplot = sns.scatterplot(x='AST',
y='TOV',
hue='Position',
palette='Set1',
data=nba_df)
plt.title('Assists vs Turnovers (Emphasised Distance)')
plt.xlabel('Assists')
plt.ylabel('Turnovers')
plt.show()
Although I am not a big fan of incredibly dense visualisations and even though it's not the most applicable here, we demonstrate that a pairplot is effective at showing a multiplot figure across multiple variables.
pairplot_df = nba_df.loc[: ,['PS/G', 'AST', 'TRB', 'BLK', 'Position']]
nba_pairplot = sns.pairplot(pairplot_df,
hue='Position',
height=3)
plt.show()
Remember that our positions data needs some cleaning up. We'll use .ix
to access indices with certain boolean conditions, in this case we search for the offending position entries. From here, we observe that there are 2 players in the SF-SG role and 1 in PG-SG.
SF_SG = nba_df.ix[nba_df['Position']=='SF-SG']
PG_SG = nba_df.ix[nba_df['Position']=='PG-SG']
print(SF_SG)
print(PG_SG)
To fix this, we will simply re-assign players in these positions to the prefix of their combined role names. To accomplish this, Pandas dataframes can use .at
to manipulate specific indices of data. The plot is easier to read after this fix.
nba_df.at[75, 'Position'] = 'SF'
nba_df.at[440, 'Position'] = 'SF'
nba_df.at[100, 'Position'] = 'PG'
pairplot_df = nba_df.loc[: ,['PS/G', 'AST', 'TRB', 'BLK', 'Position']]
nba_pairplot = sns.pairplot(pairplot_df,
hue='Position',
height=3)
plt.show()
Let's try running a quick and dirty analysis. jointplots are particularly nice at showing layered information of our data distribution and comes with different arguments for kind
. In this case, we use reg
for regression.
Let's look at whether inefficient 3 Point shooting negatively impacts overall field accuracy. Some players are known to 'chuck' long ranged shots at first opportunity and are less statistically efficient because of this. We'll take a look at whether this inefficiency also translates to their overall accuracy.
sns.set(font_scale=1, style = 'darkgrid', context='notebook')
scoring_jointplot = sns.jointplot(x='3P%',
y='FG%',
height=8,
kind='reg',
data=nba_df)
plt.show()
Based on what we can see here, this figure can be improved. For example, some players (especially bigger players) do not shoot many long ranged shots, and never land a shot at all. In addition to this, six players have an incredible long range accuracy of 100%! Let's check if this outlier is valid.
We can constrain our question a little more and instead look specifically at players who take more than one attempt per game, on average.
nba_df = nba_df.ix[nba_df['3PA']>1]
sns.set(font_scale=1, style = 'darkgrid', context='notebook')
scoring_jointplot = sns.jointplot(x='3P%',
y='FG%',
height=8,
kind='reg',
data=nba_df)
plt.show()
After filtration, it definitely looks like some players do not have accuracy much higher than 60% in both overall and long range shots. There are also a couple of players who are incredibly inaccurate with both shots too - and because we constrained this to at least one 3 point attempt across an average of the entire years' games, we can be moderately confident that these are acceptable.
Now let's tackle this topic from a slightly different angle, and look instead at volume. Are there players who shoot a high volume of 3 Point shots that are also accurate and efficient overall? We can guess who might pop up here, but first for posterity, we will present jointplots with a contour graph (kernel density estimate) and a hexbin plot.
scoring_jointplot = sns.jointplot(x='3PA',
y='FG%',
height=8,
kind='kde',
data=nba_df)
plt.show()
scoring_jointplot = sns.jointplot(x='3PA',
y='FG%',
height=8,
kind='hex',
color='#00ff99',
data=nba_df)
plt.show()
These plots are great for observing density in a two dimensional manner if your data is particularly dense (ours is not quite there). Contour plots are also nice to represent three dimensional distributions (think about classic gradient descent visualisations).
Hexagon binning may seem alien at first, but they can be more effective at representing discrete groups depending on your data (some nice information on them can be found here).
Based on our new understanding of player shooting, we can also see that there is a slightly positive relationship between our variables. Let's run a regression again to see what this might look like.
scoring_jointplot = sns.jointplot(x='3PA', y='FG%', height=8, kind='reg', data=nba_df)
plt.show()
We can see that at there are 4 players that take more than 8 long ranged shots a game and still maintain an overall accuracy comfortably above 40%. Let's take a look at who they are, once again using .ix
to find them.
god_shooters = nba_df.ix[nba_df['3PA']>8]
print(god_shooters)
At this point, we've almost answered our question. We can assume that the two points on the plot at the farthest right end are Steph Curry and James Harden.
But we may want to visualise this in our data. In order to do this,we will opt for a superpositioned plot with just points at the coordinates for Curry and Harden. We find that python visualisation libraries are somewhat unintuitive for such needs, but if you want to label a set of multiple points, this is one way to accomplish it.
To strike it home, let's also annotate our plot at these points with some text.
Finally, we can notice in the graph above that although we do not have any players taking less than 1 shot a game, the plot is still beginning at 0. This occurs in many of the graphs we generated and is subjectively quite unaesthetic. We can censor our figure's distribution to reflect this better.
# Set coordinates for hightlighting
curry_x = nba_df.loc[146, '3PA']
curry_y = nba_df.loc[146, 'FG%']
harden_x = nba_df.loc[232, '3PA']
harden_y = nba_df.loc[232, 'FG%']
# Draw main scatterplot
scoring_scatterplot = sns.regplot(x='3PA',
y='FG%',
data=nba_df)
# Draw superimposed plots
scoring_overlay = plt.scatter(curry_x,
curry_y,
color = 'blue')
scoring_overlay = plt.scatter(harden_x,
harden_y,
color = 'red')
# Label plot
scoring_scatterplot.text(curry_x,
curry_y,
'ofc Curry ',
horizontalalignment='right',
size='medium',
color='black',
weight='semibold')
scoring_scatterplot.text(harden_x,
harden_y,
' harden too',
horizontalalignment='left',
size='medium',
color='black',
weight='semibold')
plt.title('3 Point Attempts vs Field Goal Accuracy AKA Curry & Harden are OP')
plt.xlabel('3 Pointers Attempted per Game')
plt.ylabel('Field Goal Accuracy (%)')
# Apply censor
scoring_scatterplot.set(xlim=(1, None))
plt.show()
Another way you can highlight larger sets of individual points of interest is to adopt for a grouping. By setting up another column in your dataframe to assign boolean (or group identifier) values, you can set seaborn's visualisation to assign hues by group. You can try this during the excercise session now.
And that is the gist of some basic visualisation methods using some of the scipy stack. Using these rudimentary building blocks, you can later move onto more complex visualisations (that we unfortunately didn't have quite enough time for in this workshop), such as overlapping figures e.g. ridgeplots or work at a lower level with animated visualisations directly in matplotlib.
We will now move onto the primary dataset for the remainder of the workshop. The goal here is to use the tools just presented to figure out what the data actually is, and how we can work with it. To ease in, we'll take a quick look at the dataframe.
df_gm.head()
df_gm.dtypes
So the data is from a game - Path of Exile to be precise. The data was acquired from gagazet's kaggle. You could just look at the kaggle page to derive meaning of the data - but try and consider what these data might mean through some exploration.
As the workshop is quite short, we will also set some broad and loose objectives for you to try and complete:
For better illustrating the functionalities of some visualization functions we will make use of fake data generated ad-hoc for serving our purposes. Here the focus will be to employ visuals for preliminary testing (or strengthening) our hypothesis.
We will now plot time series data, showing how to compare across conditions and visualizing the units composing each time series.
It follows the structure of df_ts
df_ts.head(5)
sns.set(font_scale=1.2)
# defining the axes structure
fig,axs = plt.subplots(2, 1, figsize = (15, 10))
# plotting on the defined structure
sns.lineplot(x='time'
, y='value'
, hue='condition'
, size='condition'
, style='condition'
, data=df_ts
, palette='Set2'
, ax = axs[0]
)
sns.lineplot(x='time'
, y='value'
, size='condition'
, style='condition'
, data=df_ts
, palette='Set2'
, units='individual'
, estimator=None
, ax = axs[1]
)
# adjusting the axes aesthetic
plt.suptitle('Compare time series between conditions')
for ax in axs:
ax.set_xlim(min(df_ts['time']), max(df_ts['time']))
ax.set_xlim(min(df_ts['time']), max(df_ts['time']))
ax.set_xlabel('Time')
ax.set_ylabel('Value')
# visualize
plt.show()
The shaded area in the first plot shows the the standard deviation and the lines represents the mean. In the second plot each line corresponds to an unit while color and style (as in the first plot) code for the condtions.
Which of the two plots is a 'bad visualization'?
We will now showcase various options for exploring the relationship between varibales. For our purposes we will generate a synthetic dataframe having 3 catgorical columns, 3 scalar columns and 1000 rows.
It follows the structure of df_sy
df_sy = generate_synthetic(n_categorical=3, n_scalar=3, n_rows=1000)
df_sy.head()
sns.set(font_scale=1.2)
# define the axes structure, being grid the axes will be stored in a 2d array
fig, axs = plt.subplots(2, 2, figsize=(20,20))
# plotting on the defined structure
sns.scatterplot(x='scalar_0'
, y='scalar_1'
, data=df_sy
, ax = axs[0][0]
, alpha=0.5
)
sns.scatterplot(x='scalar_0'
, y='scalar_1'
, data=df_sy
, style='categorical_0'
, ax = axs[0][1]
)
sns.scatterplot(x='scalar_0'
, y='scalar_1'
, data=df_sy
, size='scalar_2'
, ax = axs[1][0]
)
sns.scatterplot(x='scalar_0'
, y='scalar_1'
, data=df_sy
, size='categorical_1'
, hue='scalar_2'
, hue_norm=(0,1)
, palette='magma'
, ax = axs[1][1]
)
# adjusting the axes aesthetic
axs[0][0].set_title('Simple Scatter Plot with Transparency')
axs[0][1].set_title('Simple Scatter Plot with Shape Code for Category')
axs[1][0].set_title('Simple Scatter Plot with Size Code for Scalar')
axs[1][1].set_title('Simple Scatter Plot with Size Code for Category and Color Code for Scalar')
plt.suptitle('Scatter Plots')
# visualize
plt.show()
Which reletionship between the variables can we observe?
In the first plot we adjust the alpha of the dots for overcoming the overplotting. In the second plot one categorical varibales is coded throught the shape of the dots. In the third plot we have a scalar varible represented throught the dimesnion of the dots. In the fourth plot a scalar variable is represented trhough a normalized color scale and a categorical varibale through the dimesnion of the dots.
Which plot is a 'bad visualization'?
It follows the structure of df_ln
and df_pl
df_ln.head(5)
df_pl.head(5)
sns.set(font_scale=1.2, style = 'darkgrid')
# lmplot is a figure level plot so no strucutre has to be defined
sns.lmplot(x='x'
, y='y'
, hue='condition'
, palette='colorblind'
, markers='*'
, data=df_ln
, height=10
, scatter_kws={'s': 150}
)
# adjusting the figure aesthetic
plt.title('Compare Two Linearly Related Variables Between Condtions')
plt.xlabel('x')
plt.ylabel(r'$y=wx$')
# visualize
plt.show()
lmplot
will by default try to fit a line that best describe our data. Here, colour codes for condition while the shape of the markers is arbitrarly chosen. Using the Tex syntax we can write mathematical notation on our axes or titels.
sns.set(font_scale=1.2, style ='whitegrid')
# same as above
sns.lmplot(x='x'
, y='y'
, hue='condition'
, palette='cubehelix'
, markers='+'
, order=3
, data=df_pl
, height=10
, scatter_kws={'s': 150}
)
# adjusting the axes aesthetic
plt.title('Compare Variables with Polynomial Relationship Between Condtions')
plt.xlabel('x')
plt.ylabel(r'$y=\sum_{i=0}w_ix^i$')
# visualize
plt.show()
If the relationsp between our variables is not linear lmplot
allows various options for drawing the best fit line. Being in this case a polynomial of order 3, we just need to specify the order in the plot function.
df_sy = generate_synthetic(n_categorical=0, n_scalar=10, n_rows=10000)
# create a corallation matrix and a bolead mask for hiding negative correlations
df_corr = df_sy.corr()
mask = df_corr < 0
It follows the structure of the correlation matrix and relative bolean mask.
df_corr.head(5)
mask.head()
sns.set(font_scale=1.2)
# define the axes structure
fig, axs = plt.subplots(2,1,figsize=(15,15))
# plotting on the defined structure
sns.heatmap(data=df_corr
, cmap='seismic'
, annot=True
, ax = axs[0]
)
sns.heatmap(data=df_corr
, cmap='coolwarm'
, annot=True
, mask=mask
, ax = axs[1]
)
# adjusting the axes aesthetic
axs[0].set_title('Features Pearson Correlation Matrix')
axs[1].set_title('Features Pearson Correlation Matrix - Masked Negative Correlations')
plt.suptitle('Visualize the relationships between multiple varibales')
# visualize
plt.show()
Heatmap allows to visualize the numerical annotation relative to each cell as well as applying a mask for hiding cells based on their value.
Is the color coding employed here approprihated?
df_sy = generate_synthetic(n_categorical=2, n_scalar=1, n_rows=1000)
sns.set(font_scale=1.2, style='dark')
# define the axes structure
fig, axs = plt.subplots(3,1,figsize=(15,15))
# plotting on the defined structure
sns.boxplot(x='categorical_0'
, y='scalar_0'
, palette='colorblind'
, hue='categorical_1'
, ax=axs[0]
, data=df_sy
)
sns.boxenplot(x='categorical_0'
, y='scalar_0'
, palette='colorblind'
, ax=axs[1]
, data=df_sy
)
sns.boxplot(x='categorical_0'
, y='scalar_0'
, palette='colorblind'
, ax=axs[2]
, data=df_sy
)
sns.swarmplot(x='categorical_0'
, y='scalar_0'
, cmap='colorblind'
, ax=axs[2]
, data=df_sy
)
# adjusting the axes aesthetic
plt.suptitle('Comparison of a Scalar Variable Across Multiple Categories')
# visualize
plt.show()
In the first plot it we see how to visually test the impact of two factors (with 3 levels each) on a scalar varibale. The second visualization illustrate the boxenplots, usefull when the ammount of data to represent is large and it is relevant to see a representation of the quantiles. In the third plot we sovra-imposed a boxplot and a swarmplot obtaining a result similar to the boxenplot but with more detail.
Which 'imperfection' do the three plots have?
Using the df_gm
formulate your own hypothesis and test it visually
It follows some informations regarding the df_gm
df_gm.head(2)
df_gm.dtypes
df_gm.info()
Here the focus won't be on the machine learning part but rather on how we can employ the visualizations we have seen so far for evaluating the performnce of the model. Differently for the previous sections, we will illustrate how in most of the case seaborn can work with simple itearables withouth the need of Pandas dataframes.
classifiers = {'K-nearest Neighbors' : KNN(n_jobs = -1)
, 'Logistic Regression' : LR(max_iter = 200, class_weight = 'balanced', solver='sag', n_jobs = -1)
, 'Random Forest' : RF(class_weight = 'balanced', n_jobs = -1)
, 'Multilayer Perceptron' : MLPC(max_iter = 200)
}
X, y = make_classification(n_samples = 5000, n_features=15, n_informative=5, n_redundant=5)
X_validation, y_validation, X_test, y_test = generating_validation_test(X, y)
In the first part we will show how to compare the performance of different algorithms through boxplots while in the second part we will illustrate the use of heatmaps for detailing the performance of our best model.
splitter = StratifiedShuffleSplit(n_splits=5)
scores = []
names = []
for name, classifier in classifiers.items():
for train_index, test_index in splitter.split(X, y):
classifier.fit(X[train_index], y[train_index])
scores.append(classifier.score(X[test_index], y[test_index]))
names.append(name)
It follows the structure of scores
and names
scores
names
sns.set(font_scale=1.2)
fig, ax = plt.subplots(figsize = (15, 10))
sns.boxplot(x = names
, y = scores
, orient = 'v'
, ax = ax)
plt.title('Model Comparison on Average Accuracy')
plt.xlabel('Classifiers')
plt.ylabel('Accuracy %')
plt.show()
splitter = StratifiedShuffleSplit(n_splits=3)
param_grid = {'hidden_layer_sizes' : [(10, 10, 10), (50, 50, 50), (100, 100, 100)]
, 'activation' : ['tanh', 'relu']
, 'alpha' : [1.0, 0.1, 0.001, 0.0001]
}
searcher = GridSearchCV(estimator=classifiers['Multilayer Perceptron']
, param_grid=param_grid
, cv=splitter
, scoring = 'roc_auc'
, n_jobs = 2
)
searcher.fit(X_validation, y_validation)
best_estimator = searcher.best_estimator_
y_predicted = best_estimator.predict(X_test)
cm = confusion_matrix(y_test, y_predicted)
It follows the structure of cm
cm
sns.set(font_scale=1.2)
fig, axs = plt.subplots(1, 2, figsize = (20, 10))
sns.heatmap(cm
, annot = True
, cmap='coolwarm'
, ax=axs[0]
)
sns.heatmap(cm / cm.sum(axis=1)[:, np.newaxis]
, annot = True
, cmap='coolwarm'
, ax=axs[1]
)
for ax in axs:
ax.set_xticks([0.5, 1.5], ['Positive', 'Negative'])
ax.set_yticks([0.5, 1.5], ['Negative', 'Positive'])
ax.set_xlabel('Predicted Labels')
ax.set_ylabel('True Labels')
axs[0].set_title('Confusion Matrix Multilayer Perceptron')
axs[1].set_title('Normalized Confusion Matrix Multilayer Perceptron')
plt.suptitle('Classifier Accuracy: {}'.format(best_estimator.score(X_test, y_test)))
plt.show()
Focusing on the heatmap, the first plot report the non normalized values while the second does. Here there is not much difference between the two, but in case of high imbalance in the labels distribution the colormap wouldn't be representative of the performance of the algorithm.
We will exploit now the previously presented lineplot for visualizing how an algorithm perform in the process of learning the best function for separating the categories of our sample.
# the ffn with best hyperparameters found by the grid search
best_estimator
# define the iterables where we will store our values
loss_v = []
# accuracy
accuracy_v = []
accuracy_ts = []
# f1
f1_v = []
f1_ts = []
# precision
precision_v = []
precision_ts = []
# recall
recall_v = []
recall_ts = []
epochs = [i for i in range(150)]
# refit the model with a warm start allowing for retrieving metrics at each epoch
model = MLPC(activation='relu', alpha=0.001, hidden_layer_sizes=(100, 100, 100), max_iter=1, warm_start=True)
for epoch in epochs:
model.fit(X_validation, y_validation)
v_predicted = model.predict(X_validation)
ts_predicted = model.predict(X_test)
loss_v.append(model.loss_)
# accuracy
accuracy_v.append(model.score(X_validation, y_validation))
accuracy_ts.append(model.score(X_test, y_test))
# f1
f1_v.append(f1_score(v_predicted, y_validation))
f1_ts.append(f1_score(ts_predicted, y_test))
#precision
precision_v.append(precision_score(v_predicted, y_validation))
precision_ts.append(precision_score(ts_predicted, y_test))
#recall
recall_v.append(recall_score(v_predicted, y_validation))
recall_ts.append(recall_score(ts_predicted, y_test))
sns.set(font_scale=1.2)
fig, axs = plt.subplots(3, 1, figsize = (20, 25))
# loss
sns.lineplot(y=loss_v
, x=epochs
, label='Loss Validation'
, ax=axs[0]
)
# accuracy
sns.lineplot(y=accuracy_v
, x=epochs
, label='Accuracy Validation'
, ax=axs[1]
)
sns.lineplot(y=accuracy_ts
, x=epochs
, label = 'Accuracy Test'
, ax=axs[1]
)
# f1, precision, recall
sns.lineplot(y=f1_v
, x=epochs
, label='F1 Validation'
, color='red'
, ax=axs[2]
)
sns.lineplot(y=f1_ts
, x=epochs
, label = 'F1 Test'
, color='darkred'
, ax=axs[2]
)
sns.lineplot(y=precision_v
, x=epochs
, label='Precision Validation'
, color='green'
, ax=axs[2]
)
sns.lineplot(y=precision_ts
, x=epochs
, label='Precision Test'
, color='darkgreen'
, ax=axs[2]
)
sns.lineplot(y=recall_v
, x=epochs
, label='Recall Validation'
, color='blue'
, ax=axs[2]
)
sns.lineplot(y=recall_ts
, x=epochs
, label='Recall Test'
, color='darkblue'
, ax=axs[2]
)
# control axes
for ax in axs:
ax.set_xlabel('Epochs')
ax.set_xlim(min(epochs), max(epochs))
axs[0].set_title('Loss over Epochs for Validation Set')
axs[0].set_ylabel('Loss')
axs[1].set_title('Accuracy over Epochs for Validation and Test Sets')
axs[1].set_ylabel('Accuracy')
axs[2].set_title('F1, Precision and Recall over Epochs for Validation and Test Sets')
axs[2].set_ylabel('F1 - Precision - Recall')
plt.suptitle('Learning Process of Multilayer Perceptron Classifier')
plt.legend()
plt.show()
Here we used overlapping plots for visualizing the various time series on the same axis, including an extra iterable specifying the hue we could have limited the verbosity of the code.
The last plot is very interesting and informative but what is wrong about it?
n_clusters = [i for i in range(2, 8)]
inertias = []
for n in n_clusters:
clusterer = KMeans(n_clusters = n).fit(X)
inertias.append(clusterer.inertia_)
sns.set(font_scale=1.2)
fig, ax = plt.subplots(figsize=(15, 10))
sns.pointplot(y=inertias
, x=n_clusters
, ax=ax
)
ax.set_xlabel('Number of clusters')
ax.set_ylabel('Inertia')
plt.title('Elbow Method for Clusters Selection')
plt.show()
This an example of a very 'dubious' evaluation technique represented through a good visualization, why?
We will now visualize more in detail the errors in which the algorithm incoured, pooled and for each class, trying to overcome the overplotting issue and understand the features related to the errors.
It follows information on colors_er
y_predicted = classifiers['Logistic Regression'].fit(X_validation, y_validation).predict(X_test)
# we ran PCA for extracting 2 major components from our feature set
major_components = PCA(n_components = 2).fit_transform(X_test)
colors_gt = ['b' if label == 0 else 'y' for label in y_test]
colors_er = ['r' if prediction != ground_truth else 'w' for prediction, ground_truth in zip(y_predicted, y_test)]
colors_er_0 = ['r' if prediction != ground_truth and prediction == 0 else 'w' for prediction, ground_truth in zip(y_predicted, y_test)]
colors_er_1 = ['r' if prediction != ground_truth and prediction == 1 else 'w' for prediction, ground_truth in zip(y_predicted, y_test)]
colors_er[:10]
sns.set(font_scale=1.2)
fig, axs = plt.subplots(2, 2, figsize=(15, 15))
sns.regplot(major_components[:,0]
, major_components[:,1]
, fit_reg=False
, scatter_kws={'facecolors':colors_gt, 's':30}
, ax=axs[0][0]
)
sns.regplot(major_components[:,0]
, major_components[:,1]
, fit_reg=False
, scatter_kws={'facecolors':colors_er, 's':30}
, ax=axs[0][1]
, marker='+'
)
sns.regplot(major_components[:,0]
, major_components[:,1]
, fit_reg=False
, scatter_kws={'facecolors':colors_er_0, 's':30}
, ax=axs[1][0]
, marker='+'
)
sns.regplot(major_components[:,0]
, major_components[:,1]
, fit_reg=False
, scatter_kws={'facecolors':colors_er_1, 's':30}
, ax=axs[1][1]
, marker='+'
)
for ax in axs.flatten():
ax.set_xlabel('Component 1')
ax.set_ylabel('Component 2')
axs[0][0].set_title('Ground Truth')
axs[0][1].set_title('Misclassifications')
axs[1][0].set_title('Misclassifications Label 0')
axs[1][1].set_title('Misclassifications Label 1')
plt.suptitle('Overall Performance of Multilayer Perceptron')
plt.show()
There is an imperfection in the title of the last two plots, what is it?
locations_er_0 = []
locations_er_1 = []
for location, prediction, ground_truth in zip(major_components, y_predicted, y_test):
if prediction != ground_truth and prediction == 0:
locations_er_0.append(location)
elif prediction != ground_truth and prediction == 1:
locations_er_1.append(location)
locations_er_0 = np.array(locations_er_0)
locations_er_1 = np.array(locations_er_1)
sns.set(font_scale=1.2)
fig, axs = plt.subplots(1, 2, figsize=(25, 15))
sns.kdeplot(locations_er_0[:,0]
, locations_er_0[:,1]
, cmap='Reds'
, shade=True
, shade_lowest=False
, ax=axs[0]
)
sns.kdeplot(locations_er_1[:,0]
, locations_er_1[:,1]
, cmap='Reds'
, shade=True
, shade_lowest=False
, ax=axs[1]
)
# adjusting the axes aesthetic keeping the scale of the plot equal between the two conditions
for ax in axs.flatten():
ax.set_xlabel('Component 1')
ax.set_ylabel('Component 2')
ax.set_xlim(min(major_components[:,0]), max(major_components[:,0]))
ax.set_ylim(min(major_components[:,1]), max(major_components[:,1]))
axs[0].set_title('KDE Misclassifications Label 0')
axs[1].set_title('KDE Misclassifications Label 1')
plt.suptitle('Kernel Desnity Estimates Misclassifications Multilayer Perceptron')
plt.show()
components_corr = np.append(X_test, major_components, axis = 1)
components_corr = pd.DataFrame(components_corr).corr()
components_corr = components_corr[[15, 16]]
components_corr.head(5)
sns.set(font_scale=1.2)
fig, ax = plt.subplots(figsize=(5, 15))
sns.heatmap(components_corr
, annot = True
, cmap='seismic'
, ax = ax
)
plt.title('Pearson Correlation Major Components and Original Features')
ax.set_xticklabels(labels=['Major 1', 'Major 2'])
ax.set_xlabel('Major Components')
ax.set_ylabel('Original Features')
plt.show()
In the following cells you will find arrays containing preprocessed data. You can use one of the out-of-the-box models that scikit-learn
offers in the attempt to predict if a player died or not.
In case you prefer doing your own preprocessing (suggested) you can use the df_ml
, which has been already prepared for being in a machine learning friendly format.
The goal here is to pick a strategy for solving the classification problem and using a visual for validating the outcomes of your strategy.
It follows some information on df_ml
df_ml.head(2)
df_ml.dtypes
df_ml.info()
X, y = generate_X_y(df_ml, list(df_ml.drop('dead', 1)), 'dead')
X_validation, y_validation, X_test, y_test = generating_validation_test(X, y)


HEXED A film review by James Berardinelli Copyright 1993 James Berardinelli
Running Length: 1:33 Rated: R (Language, nudity, sexual situations)
Starring: Ayre Gross, Claudia Christian, Adrienne Shelly, Norman Fell Director: Alan Spencer Producers: Marc S. Fischer and Louis G. Friedman Screenplay: Alan Spencer Music: Lance Rubin Released by Columbia Pictures
Matthew Welsh (Ayre Gross), a clerk at the Holiday Palace Hotel, is a dreamer who has trouble keeping straight the line between fantasy and reality. His life takes a turn for the bizarre when Hexina (Claudia Christian), the world-famous model, checks into the hotel. Through an elaborate ruse, Matthew manages to lure her back to his apartment for a night of wild, uninhibited passion. Unfortunately for him, sex isn't the only thing Hexina gets him involved in. Matthew soon learns that she also has a thing for knives and guns.
Occasionally, small-budget comedies with relatively-unknown actors can be charming and entertaining (witness 1992's THE CUTTING EDGE with Moira Kelly and D. B. Sweeney). HEXED is not such a film. While it obviously cost little to produce, and offers Norman Fell as its best-known name, "entertaining" is about the last word I would use to describe it. Offensive, humorless, and tiresome are more appropriate adjectives.
While comedies are not generally known for advocating a social agenda, HEXED is reprehensible in the attitude it presents. Originally scheduled for a 1992 release, it was temporarily shelved after the Los Angeles riots. The reason is obvious: a scene where uniformed police officers beat an unarmed man. Designed to parody the Rodney King incident (with the line by the white victim of "I'm a Republican" supposedly providing the irony), it's unclear why this moment wasn't relegated to the cutting room floor--its presence is disgraceful. Only the most twisted mind will find humor in lampooning such an event.
On top of that, the film's attitude toward blacks is disturbing.  In no scene is he presented in a positive light. While an injustice like this might go unnoticed in other movies, attention is drawn to it by the aforementioned parody of the King beating. I won't go so far as to charge director/ writer Alan Spencer with racism, but he is guilty at least of insensitivity.
HEXED is yet another in a long line of supposed comedies that doesn't contain a single humorous line. The placing and intent of the jokes is obvious, but they're all so juvenile that even the easily-amused will discover little to make them laugh. Most TV sitcoms serve up better fare. This film did not elicit one chuckle from me, and that's rare for even the most lame of movies.
As a satire, HEXED is no more successful than it is as anything else. There are quick visual references to BASIC INSTINCT, FATAL ATTRACTION, and a number of other slasher/exploitation films, but none of these are memorable or amusing. They lack teeth and wit.
Finally, a word should be said about the performers. They deserve to be in a box-office bomb. They have no range. It's a fair indication of how intolerable they are to say that Norman Fell's performance is among the most credible. Claudia Christian does a fine job by the standards of high camp, but even her die-hard fans would be hard-pressed to justify sitting through ninety minutes of this dross simply to listen to her screeching lines, and watch her making faces and strutting around in a black leather mini-skirt.
It's curious that HEXED was released to theaters in the first place. It's the kind of movie that, on those occasions when it gets filmed, usually goes straight to video. From the reception it got from the pitifully small crowd on "opening night", it won't take long to reach there. This review is a warning not only to miss it on the big screen, but to give it an equally wide berth when it's available for the smaller one.
Rating: 1.9 (F, *)
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


| March 5, 2020 |
|
Afternoon Arrivals / Registration |
|
| 1:00 pm - 5:00 pm |
Registration |
Aspen |
| 7:00 pm - 10:00 pm |
Welcome Reception & Dinner (Dress is Casual Resort) |
Handelbar Lift (Hotel) |
| March 6, 2020 |
| 7:00 am - 10:00 am |
Breakfast at Leisure |
Westbank Grill |
| 8:00 am - 3:00 pm |
Leisure Time to Enjoy Hosted Individual Activities |
Meet in Lobby 15 min prior |
| 6:30 pm - 9:00 pm |
Off-site Dinner |
Meet in Lobby 15 min prior/Heading to town |
March 7, 2020 |
| 7:00 am - 10:00 am |
Breakfast at Leisure |
Westbank Grill |
| 8:00 am - 3:00 pm |
Leisure Time to Enjoy Hosted Individual Activities |
Meet in Lobby 15 min prior |
| 7:00 pm - 10:00 pm |
Closing Reception & Dinner (Dress is Casual Resort) |
Cotton Wood Terrace |
March 8, 2020 |
| 7:00 am - 10:00 am |
Breakfast at Leisure |
Westbank Grill |
| 9:00 am - 5:00 pm |
Check out/ Airport Departures (See Departure Signage) |
|


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Note that the summary field can use markdown markup.
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|
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| PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES |
See:
Description
| Interface Summary | |
|---|---|
| SolAccessor<V extends Var<Info>,Info> | SolAccessor provides access to the values of the
variables from a system of constraints. |
| SolReader<V extends Var<Info>,Info> | SolReader provides read-only access to the (possibly
partial) solution of a system of constraints. |
| Class Summary | |
|---|---|
| Constraint<V extends Var<Info>,Info> | Constraint models a constraint between several
variables. |
| ConstraintSystem<V extends Var<Info>,Info> | ConstraintSystem is an efficient solver for a system
of constraints. |
| CtConstraint<V extends Var<Info>,Info> | CtConstraint models a constraint of the form
"constant ct is less than the value of the
variable vd ". |
| LtConstraint<V extends Var<Info>,Info> | LtConstraint models a "less than"
constraint: the value of variable vs is smaller than
the value of variable vd , according to the order
relation from the corresponding lattice. |
| Var<Info> | Var models a variable from a system of constraints. |
Generic solver for inequality constraints over lattices.
This package provides a generic way of modeling and solving systems of inequality constraints over lattices. This package deals with constraints of the form
f(vi_1,vi_2,...,vi_m) <= <vo_1,vo_2,...,vo_n>
where the ordering relation <=
is specific to the
relevant lattices. The function f
is not defined
in a specialized declarative language: instead, it is defined as a
Java method that reads the values of the input variables vi_1,
..., vi_m
, computes some values, and finally joins the computed
values to the output variables vo_1, ..., vo_m
; the
constraints access the values of the variables through a well-defined
interface (SolAccessor
). The
programmer can define new constraints by subclassing the Constraint
class.
A system of constraints may involve variables that take values in different lattices: e.g., the values of some variables are sets of atoms, while the values of some other variables are binary relations between atoms. As such, this package generalizes usual set constraint solvers. Of course, specialized set solvers may be much faster and you are encouraged to take a look over them too.
The essential classes from this package are
Var
- variables; also, each variable
carries with it the operations of the lattice it takes values in;
Constraint
- general form of
a constraint; each constraint should subclass this class;
ConstraintSystem
- an efficient constraint solver.
Classes from this package are parameterized over the type of variables
(always a subtype of Var
) and the common supertype of the
classes representing the value lattices (in the extreme case, if there
is no better supertype, this type parameter can be Object
).
If we have several value lattices, certain type errors cannot be
detected statically: e.g., a constraint that states that a binary
relation is included in a set of atoms will trigger a dynamic
exception. The program should be careful about this aspect when
writting constraints.
To use this package, the programmer must follow the following steps
Var
for each
kind of variables: e.g., one subclass for the variables that have set
values and one subclass for the variables that have relation values.
The essential point here is to define the lattice-related operations
copy
and join
. In addition, it is usually a good
idea to add some debug information; e.g., override the default Var.toString
method.
Constraint
for each kind of constraints.
This package already contains two very basic kinds of constraints
(CtConstraint
and LtConstraint
), but you usually need to
define your own constraints too. Here is a sample constraint
implementation: simplified SetConstraints.IntersectConstraint.
ConstraintSystem
to solve them.
[ ConstraintSystem
works in two steps: first, it computes some data structures that allow
the efficient computation of a fixed-point solution; next, we can
solve it several times, at different moments in time: if some of the
constraints use external values that change, the solutions may be
different. So, ConstraintSystem
is an optimized constraint system schema that
can be efficiently solved several times. ]
jpaul.Constraints.SetConstraints
|
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Healthcare Minister refuses to drop idea of financing healthcare at budget deficit’s expense
by LETALatvian Healthcare Minister Ilze Viņķele is committed to meeting with Prime Minister Krišjānis Kariņš to discuss the idea of increasing healthcare funding at the expense of state budget deficit.
This much the minister promised on Monday, 25 November, at the conference of patient organization. According to her, the promised wage increase for healthcare workers can be secured by increasing state budget deficit. The minister mentioned at the conference that she agrees with trade unions representing doctors.
Commenting on the unity of healthcare workers, the minister said this is a historic moment.
«The louder the voices of doctors ring, the greater the possibility of them being heard,» said Viņķele.
When asked to comment the recent signature collection process to initiate Saeima’s dismissal, the minister said re-election of the parliament would not bring residents desired changes.
At the same time, she admits the law adopted at the beginning of the year on healthcare funding was a political mistake, stressing that in such cases legislators should avoid adding any percentage increases of funding in legislative drafts. The minister also stresses the lack of experience of young deputies. She considers this one of the reasons for the mistake.
Viņķele also mentioned that together with the State Chancellery a new prototype for wages is being developed. This new model may take into account workload equivalent and the type of job and daily duties.
As previously reported, Attīstībai/Par! political parties association had previously reported allocating EUR 60 million needed to raise wages for doctors at the expense of budget deficit. However, Prime Minister Krišjānis Kariņš did not support this proposal and asked Viņķele to focus on her main objective – sorting the healthcare system.
Finance Ministry stressed that if the government decides to breach European Union’s permitted deficit level, it is necessary to keep in mind long-reaching and expensive consequences for the state budget. Finance Ministry invited policy-makers «to be patient and understand that state budget expenditures have to be measured together with state budget revenue».
More on this topic: Attīstībai/Par! political party invites raising doctors’ wages at budget deficit’s expense
On 14 November, Latvia’s Saeima approved in the final reading the state budget for 2020, allocating EUR 60 million for doctors’ wages.


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- CARET - Static variable in interface mlp.PomParserConstants
-
RegularExpression Id.
- CASES - Static variable in interface mlp.PomParserConstants
-
RegularExpression Id.
- changeFeatureName(String, String) - Method in class mlp.FeatureSet
-
Changes the current name of the specified feature to a new name, if
the specified feature exists and the provided new name is not already
a name of an existing feature.
- changeNames(Map<String, List<FeatureSet>>, Map<String, String>, Map<String, String>) - Static method in class mlp.LexiconFactory
-
In the specified lexicon map, this method
changes the names of specified features to new names, and
also changes the names of the specified Feature-Set names,
and return the modified lexicon-map
- CharStream - Interface in mlp
-
This interface describes a character stream that maintains line and
column number positions of the characters.
- cleanRawTexUnicodeData(String, String) - Static method in class mlp.LatexUnicode
-
Reads raw Tex-and-Unicode symbols from the specified input file,
cleans the data and converts it into a tidy format, and
writes the results in the specified file.
- cleanRawTexUnicodeData(String) - Static method in class mlp.LatexUnicode
-
Reads a file where each meaningful line starts with a Unicode (u+XXXXX)
and has a Latex command followed by explanations.
- clearLexicon() - Method in class mlp.PomParser
-
Clears the current lexicon.
- collectionToString(Collection<String>, String) - Static method in class mlp.LexiconUtility
-
Concatenates the String elements of the specified input collection,
separating them with the specified inlineElementSep.
- collectionToString(Collection<String>, String) - Static method in class mlp.MlpUtility
-
Concatenates the String elements of the specified input collection,
separating them with the specified separator.
- COLON - Static variable in interface mlp.PomParserConstants
-
RegularExpression Id.
- COMMA - Static variable in interface mlp.PomParserConstants
-
RegularExpression Id.
- constructMacro(String) - Static method in class mlp.LatexMacro
-
Constructs a LatexMacro object from the specified
"\newcommand ..." input string.
- contains(FeatureSet) - Method in class mlp.FeatureSet
-
Checks if the underlying FeatureSet object contains the specified
featureSet object.
- CONTROLLED_SPACE - Static variable in interface mlp.PomParserConstants
-
RegularExpression Id.
- createCleanFilesAndLexicons(String) - Static method in class mlp.LexiconUtility
-
Reads an array of raw files from the raw-files folder in the
specified reference data folder,
and converts each raw version into a clean version and puts it in
the clean files folder.
- createLexicon(String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Reads the contents of the lexicon stored in the specified file,
and creates and returns a Lexicon object.
- createLexicon(Path, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexicon() except that the lexicon is in the specified
path.
- createLexicon(String, String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexicon() except this time each lexicon entry key
ends (on the same line) with the specified entrySignalEnd.
- createLexicon(Path, String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexicon() except this time each lexicon entry key
ends (on the same line) with the specified entrySignalEnd,
and the file is specified by the Path input.
- createLexiconMap(Path, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Reads the contents of the lexicon stored in the file specified by
the Path argument,
and creates and returns a map, which has one map entry per lexicon entry.
- createLexiconMap(String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexiconMap() except that the lexicon
entry keys extend to the end of the line, excluding spaces and
end-of-line comments.
- createLexiconMap(Path, String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexiconMap() except that the lexicon
is in the specified filename.
- createLexiconMap(String, String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexiconMap() except that the lexicon
is in the specified filename.
- createLexiconMap(String[], String, String, String, String) - Static method in class mlp.LexiconFactory
-
Like other createLexiconMap() except that the lexicon
entry keys extend to the end of the line, excluding spaces and
end-of-line comments.
- createLexiconMap(String[], String, String, String, String, String) - Static method in class mlp.LexiconFactory
-
Reads the contents of the lexicon represented by the specified
lines array,
and creates and returns a map, which has one map entry per lexicon entry.
- createStructuredUnicodeMathDB(String, String) - Static method in class mlp.LatexUnicode
-
Reads the input file where each non-header line is of the form:
unicode <> TeX command <> source <> description
A header line is one that ends with ":" and does not not start
with a unicode (u+XXXXX).
- createUnicodeMathMap(String) - Static method in class mlp.LatexUnicode
-
Reads the input file where each non-header line is of the form:
unicode <> TeX command <> source <> description
A header line is one that ends with ":" and does not not start
with a unicode (u+XXXXX).
- csv2Map(List<String>) - Static method in class mlp.MlpUtility
-
Converts a List of comma-separated key-value pairs, one pair per line,
into a map with the corresponding keys and values.
- curChar - Variable in class mlp.PomParserTokenManager
-
- currentToken - Variable in exception mlp.ParseException
-
This is the last token that has been consumed successfully.


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