arcarchit

def find_strictly_greater(ary, no):
    left = 0
    right = len(ary) - 1
    # F F F T T T T T
    # We want to find first T
    # We don't want to miss T

    while left < right :
        mid = left + (right-left)/2 # This is bias towards left and we are incrementing left so good

arcarchit

import numpy as np
from scipy.stats import t, zscore


def grubbs(X, test='two-tailed', alpha=0.05):

    '''
    Performs Grubbs' test for outliers recursively until the null hypothesis is
    true.

arcarchit

\* Minimize food cost *\
Minimize
Total_Cost: 0.008 Beef_percentage + 0.013 chicken_percentag
Subject To
Fat_Constraint: 0.1 Beef_percentage + 0.08 chicken_percentag >= 6
Fiber_Constrint: 0.005 Beef_percentage + 0.001 chicken_percentag <= 2
Protine_Constraint: 0.2 Beef_percentage + 0.1 chicken_percentag >= 8
Salt_constraint: 0.005 Beef_percentage + 0.002 chicken_percentag <= 0.4
Total_Sum: Beef_percentage + chicken_percentag = 100
End

arcarchit

---
title: "ARIMA model"
author: "Archit Vora"
date: "April 3, 2018"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

arcarchit

from math import sqrt
phi = (1 + sqrt(5))/2
resphi = 2 - phi
 
# a and b are the current bounds; the minimum is between them.
# c is the center pointer pushed slightly left towards a
def goldenSectionSearch(f, a, c, b, absolutePrecision):
    if abs(a - b) < absolutePrecision:
        return (a + b)/2
    # Create a new possible center, in the area between c and b, pushed against c

arcarchit

arcarchit

arcarchit

import numpy as np
import scipy.stats as stats
from matplotlib import pyplot as plt

class BetaThompson:
    def __init__(self, num_bandits, prior_a, prior_b):
        self.num_bandits = num_bandits
        self.a = prior_a
        self.b = prior_b