Marcel Boersma boersmamarcel

## comparison.csv

          
            Feature
            RPC
            REST API
            WebSocket

            
              Performance
              (Binary, HTTP/2)
              Good (Text-based JSON, HTTP/1.1)
              (Low overhead after handshake)

            
              Communication
              RPC (Unary, Streaming)
              Request/Response
              Full-duplex (Bidirectional messages)

            
              Payload Format
              Protocol Buffers (Compact binary)
              JSON (Human-readable text)
              Custom (Binary or text)

            
              Ease of Use
              Moderate (Requires .proto definitions)
              Easy (Widely understood)
              Moderate (Connection management)

            
              Primary Use Case
              High-performance microservices
              Public & simple web APIs
              Real-time interactive apps

## LinearRegression.java
package regression;

import java.io.BufferedInputStream;
import java.io.BufferedReader;
import java.io.DataInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;

## theta.csv

          
            0.01
            0.03
            4
            2
            0.9
            2
            0.8
            0.9
            2
            3
            0.1

## diabetes.csv

          
            151
            0.038076
            0.05068
            0.061696
            0.021872
            -0.044223
            -0.034821
            -0.043401
            -0.002592
            0.019908
            -0.017646

            
              75
              -0.001882
              -0.044642
              -0.051474
              -0.026328
              -0.008449
              -0.019163
              0.074412
              -0.039493
              -0.06833
              -0.092204

            
              141
              0.085299
              0.05068
              0.044451
              -0.005671
              -0.045599
              -0.034194
              -0.032356
              -0.002592
              0.002864
              -0.02593

            
              206
              -0.089063
              -0.044642
              -0.011595
              -0.036656
              0.012191
              0.024991
              -0.036038
              0.034309
              0.022692
              -0.009362

            
              135
              0.005383
              -0.044642
              -0.036385
              0.021872
              0.003935
              0.015596
              0.008142
              -0.002592
              -0.031991
              -0.046641

            
              97
              -0.092695
              -0.044642
              -0.040696
              -0.019442
              -0.068991
              -0.079288
              0.041277
              -0.076395
              -0.04118
              -0.096346

            
              138
              -0.045472
              0.05068
              -0.047163
              -0.015999
              -0.040096
              -0.0248
              0.000779
              -0.039493
              -0.062913
              -0.038357

            
              63
              0.063504
              0.05068
              -0.001895
              0.06663
              0.09062
              0.108914
              0.022869
              0.017703
              -0.035817
              0.003064

            
              110
              0.041708
              0.05068
              0.061696
              -0.040099
              -0.013953
              0.006202
              -0.028674
              -0.002592
              -0.014956
              0.011349

            
              310
              -0.0709
              -0.044642
              0.039062
              -0.033214
              -0.012577
              -0.034508
              -0.024993
              -0.002592
              0.067736
              -0.013504

## gist:4202270
package regression;

import java.io.BufferedInputStream;
import java.io.BufferedReader;
import java.io.DataInputStream;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;

## gist:2838323
##Requires a data frame with column "Name" (type as.character) and "Tags"

library(tseries)
library(igraph)

# get all the quotes for the stocks and put it in a dataframe
get.quotes.df <- function(stocks.df, start){


 stock.values <- NULL

## stocks-to-market-correlation.R
##Requires a data frame with column "Name" (type as.character) and "Tags"

library(tseries)
library(ggplot2)

# get all the quotes for the stocks and put it in a dataframe
get.quotes.df <- function(stocks.df, start){


 stock.values <- NULL

## density-stocks.r
##Requires a data frame with column "Name" (type as.character) and "Tags"

library(tseries)
library(ggplot2)

# get all the quotes for the stocks and put it in a dataframe
get.quotes.df <- function(stocks.df, start){


 stock.values <- NULL
Feature	RPC	REST API	WebSocket
Performance	(Binary, HTTP/2)	Good (Text-based JSON, HTTP/1.1)	(Low overhead after handshake)
Communication	RPC (Unary, Streaming)	Request/Response	Full-duplex (Bidirectional messages)
Payload Format	Protocol Buffers (Compact binary)	JSON (Human-readable text)	Custom (Binary or text)
Ease of Use	Moderate (Requires .proto definitions)	Easy (Widely understood)	Moderate (Connection management)
Primary Use Case	High-performance microservices	Public & simple web APIs	Real-time interactive apps
	package regression;

	import java.io.BufferedInputStream;
	import java.io.BufferedReader;
	import java.io.DataInputStream;
	import java.io.File;
	import java.io.FileInputStream;
	import java.io.FileNotFoundException;
	import java.io.IOException;
	import java.io.InputStreamReader;
151	0.038076	0.05068	0.061696	0.021872	-0.044223	-0.034821	-0.043401	-0.002592	0.019908	-0.017646
75	-0.001882	-0.044642	-0.051474	-0.026328	-0.008449	-0.019163	0.074412	-0.039493	-0.06833	-0.092204
141	0.085299	0.05068	0.044451	-0.005671	-0.045599	-0.034194	-0.032356	-0.002592	0.002864	-0.02593
206	-0.089063	-0.044642	-0.011595	-0.036656	0.012191	0.024991	-0.036038	0.034309	0.022692	-0.009362
135	0.005383	-0.044642	-0.036385	0.021872	0.003935	0.015596	0.008142	-0.002592	-0.031991	-0.046641
97	-0.092695	-0.044642	-0.040696	-0.019442	-0.068991	-0.079288	0.041277	-0.076395	-0.04118	-0.096346
138	-0.045472	0.05068	-0.047163	-0.015999	-0.040096	-0.0248	0.000779	-0.039493	-0.062913	-0.038357
63	0.063504	0.05068	-0.001895	0.06663	0.09062	0.108914	0.022869	0.017703	-0.035817	0.003064
110	0.041708	0.05068	0.061696	-0.040099	-0.013953	0.006202	-0.028674	-0.002592	-0.014956	0.011349
310	-0.0709	-0.044642	0.039062	-0.033214	-0.012577	-0.034508	-0.024993	-0.002592	0.067736	-0.013504
	##Requires a data frame with column "Name" (type as.character) and "Tags"

	library(tseries)
	library(igraph)

	# get all the quotes for the stocks and put it in a dataframe
	get.quotes.df <- function(stocks.df, start){


	stock.values <- NULL
	##Requires a data frame with column "Name" (type as.character) and "Tags"

	library(tseries)
	library(ggplot2)

	# get all the quotes for the stocks and put it in a dataframe
	get.quotes.df <- function(stocks.df, start){


	stock.values <- NULL