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import tensorflow as tf | |
import pandas as pd | |
import numpy as np | |
import matplotlib.pyplot as plt | |
msft = pd.read_csv("MSFT.csv") | |
print(msft.shape) | |
msft_returns = [] | |
for i in range(1,1259): |
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/******************************************************************************* | |
* Name : waterJugPuzzle.cpp | |
* Author : Bharddwaj Vemulapalli | |
* Date : March 13, 2020 | |
* Description : Solves the water jug puzzle. | |
* Pledge : I pledge my honor that I have abided by the Stevens Honor System. | |
******************************************************************************/ | |
#include <iostream> | |
#include <sstream> | |
#include <vector> |
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// | |
// main.cpp | |
// GameAndWatch | |
// | |
// Created by Bharddwaj Vemulapalli on 2/7/20. | |
// Copyright © 2020 Bharddwaj Vemulapalli. All rights reserved. | |
// | |
#include <iostream> | |
#include <random> |
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import random | |
def SingleDimensionWalk(x,count): | |
''' | |
Write a program to simulate a random walk in one dimension starting at 0. | |
Have your program print out the lengths of the times between returns to the starting point (returns to 0). | |
''' | |
if random.randint(0,1): | |
x+=1 | |
else: |
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package homework; | |
import java.util.Random; | |
import java.util.Stack; | |
/** | |
* @param <E> | |
*/ | |
//Bharddwaj Vemulapalli | |
//CS284 | |
//I pledge my honor that I have abided by the Stevens Honor System. | |
public class treap<E extends Comparable<E>>{ |
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def LinearRegression(x,y,round_digits): | |
''' | |
x: feature vector (Preferably Numpy Array) | |
y: dependent vector (Preferably Numpy Array) | |
round_digits: number of digits you want to round the final equation to | |
This function returns the Ordinary Least Squares Regression model. | |
''' | |
c = np.ones((len(x[0]),)) | |
A = [] | |
A.append(c) |
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import pandas as pd | |
import numpy as np | |
import csv | |
import seaborn as sns | |
import matplotlib.pyplot as plt | |
import math | |
from collections import OrderedDict | |
data = pd.read_csv("CL_June_2019.csv") | |
print(type(data)) |
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import pandas as pd | |
import numpy as np | |
import csv | |
import seaborn as sns | |
import matplotlib.pyplot as plt | |
import math | |
data = pd.read_csv("CL_June_2019.csv") | |
print(type(data)) | |
#new_data = data[59528000:59800000] |
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import pandas as pd | |
import numpy as np | |
import csv | |
import seaborn as sns | |
import matplotlib.pyplot as plt | |
import math | |
data = pd.read_csv("CL_June_2019.csv") | |
#new_data = data[59528000:59800000] |
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import pandas as pd | |
import numpy as np | |
import csv | |
import seaborn as sns | |
import matplotlib.pyplot as plt | |
import math | |
data = pd.read_csv("CL_June_2019.csv") | |
print(type(data)) | |
#new_data = data[59528000:59800000] |
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