Kenytt Avery ProfAvery

## .gitignore
burncpu
multithread
mutex
semaphore

## onethread.cpp
#include <cstdlib>
#include <iostream>

#include <pthread.h>

using std::cout;
using std::cerr;
using std::endl;

// Don't forget to compile with -pthread

## Basic Linear Regression.ipynb

      
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                ProfAvery
                / Basic Linear Regression.ipynb
            
            
              Created
              September 11, 2020 16:08
            
              
                Linear Regression with scikit-learn on the Olympic men's 100m dataset
              
          
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## forking.cpp
#include <cstdlib>
#include <iostream>

#include <sys/types.h>
#include <sys/wait.h>
#include <unistd.h>

using std::cout;
using std::cerr;
using std::endl;

## grandparent.cpp
#include <cstdlib>
#include <iostream>

#include <sys/wait.h>
#include <unistd.h>

using std::cout;
using std::endl;

int main()

## info.cpp
#include <cstdlib>
#include <iostream>

#include <sys/utsname.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <unistd.h>

using std::cout;
using std::endl;

## logging.ini
#
# Simple logging configuration for Python
#
# NOTE: Don't add spaces between comma-separated lists in this file
#

[DEFAULT]
filename = 'output.log'

[loggers]

## config1.py
app.config['DYNAMO_TABLES'] = [
    dict(
         TableName='users',
         KeySchema=[dict(AttributeName='username', KeyType='HASH')],
         AttributeDefinitions=[dict(AttributeName='username', AttributeType='S')],
         ProvisionedThroughput=dict(ReadCapacityUnits=5, WriteCapacityUnits=5)
    ), dict(
         TableName='groups',
         KeySchema=[dict(AttributeName='name', KeyType='HASH')],
         AttributeDefinitions=[dict(AttributeName='name', AttributeType='S')],

## data.csv

          
            1
            6

            
              2
              6

            
              3
              10

            
              4
              12

## hebb.py
#!/usr/bin/env python3

# Hebb Net example - Character recognition
#
# See Fausett, Example 2.8, pp. 55-56
#

TRAINING_X = [
    '#...#',
    '.#.#.',
	#include <cstdlib>
	#include <iostream>

	#include <pthread.h>

	using std::cout;
	using std::cerr;
	using std::endl;

	// Don't forget to compile with -pthread
	#include <cstdlib>
	#include <iostream>

	#include <sys/types.h>
	#include <sys/wait.h>
	#include <unistd.h>

	using std::cout;
	using std::cerr;
	using std::endl;
	#include <cstdlib>
	#include <iostream>

	#include <sys/utsname.h>
	#include <sys/types.h>
	#include <sys/stat.h>
	#include <unistd.h>

	using std::cout;
	using std::endl;
	#
	# Simple logging configuration for Python
	#
	# NOTE: Don't add spaces between comma-separated lists in this file
	#

	[DEFAULT]
	filename = 'output.log'

	[loggers]
	app.config['DYNAMO_TABLES'] = [
	dict(
	TableName='users',
	KeySchema=[dict(AttributeName='username', KeyType='HASH')],
	AttributeDefinitions=[dict(AttributeName='username', AttributeType='S')],
	ProvisionedThroughput=dict(ReadCapacityUnits=5, WriteCapacityUnits=5)
	), dict(
	TableName='groups',
	KeySchema=[dict(AttributeName='name', KeyType='HASH')],
	AttributeDefinitions=[dict(AttributeName='name', AttributeType='S')],
	#!/usr/bin/env python3

	# Hebb Net example - Character recognition
	#
	# See Fausett, Example 2.8, pp. 55-56
	#

	TRAINING_X = [
	'#...#',
	'.#.#.',