kylelrichards11

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression

train = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/test.csv")

kylelrichards11

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import OneHotEncoder

train = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/test.csv")

kylelrichards11

%% Homework 4
% Kyle Richards
clear;
clc;
%% Part A

%% dh parameters
%%
% 
% <<dh.png>>

kylelrichards11

%% This function takes the desired tip position and returns the three joint angles required

function [angles] = ikin(positions)

px = positions(1, 1);
py = positions(2, 1);
pz = positions(3, 1);

l1 = 135;
l2 = 175;

kylelrichards11

function [transformationMatrix] = dhToTransformation (a, alpha, d, theta)

transformationMatrix = [cos(theta) -sin(theta)*cos(alpha) sin(theta)*sin(alpha) a*cos(theta);
     sin(theta) cos(theta)*cos(alpha) -cos(theta)*sin(alpha) a*sin(theta);
     0 sin(alpha) cos(alpha) d;
     0 0 0 1;];

end

kylelrichards11

% Kyle Richards
% Prelab 2
% 9/5/2018

function[positions] = fwkin(a1, a2, a3)

% Set lengths of arms
L1 = 135;
L2 = 175;
L3 = 169.28;

kylelrichards11

// Kyle Richards
// 8/28/2018

/**
 * RBE3001 - Nucleo Firmware
 * See header file for more detail on this class.
 */
#include "EmptyPidServer.h"

/**