jaems33

#include <stdio.h>
#include <math.h>

double catenary(const double x, const double a) {
  return a * cosh(x / a);
}

double catenary_with_eulers(const double x, const double a) {
  return (a / 2.0) * (exp(x / a) + exp(-x / a));
}

jaems33

def fit_one_cycle(learn:Learner,
                  cyc_len:int,
                  max_lr:Union[Floats,slice]=default_lr, 
                  moms:Tuple[float,float]=(0.95,0.85), 
                  div_factor:float=25.,
                  pct_start:float=0.3, 
                  wd:float=None,
                  callbacks:Optional[CallbackList]=None, **kwargs) -> None:
    """Fit a model following the 1cycle policy."""
    max_lr = learn.lr_range(max_lr)

jaems33

import random
import numpy as np
import matplotlib.pyplot as plt


# Udacity provided code
class Robot(object):
    def __init__(self, length=20.0):
        """
        Creates robot and initializes location/orientation to 0, 0, 0.

jaems33

from copy import deepcopy


def printpaths(path, newpath):
    for old, new in zip(path, newpath):
        print('[' + ', '.join('%.3f' % x for x in old) +
              '] -> [' + ', '.join('%.3f' % x for x in new) + ']')


path = [[0, 0], [0, 1], [0, 2], [1, 2], [2, 2], [3, 2], [4, 2], [4, 3], [4, 4]]

jaems33

import numpy as np
from numpy.linalg import inv

x_observations = np.array([4000, 4260, 4550, 4860, 5110])
v_observations = np.array([280, 282, 285, 286, 290])

z = np.c_[x_observations, v_observations]

# Initial Conditions
a = 2  # Acceleration

jaems33

import numpy as np

# Initial Values
x = 50
x_dot = 5

# Standard Deviations
x_std = 0.5
x_dot_std = 0.2

jaems33

import numpy as np

A = np.array([[90, 80, 40],
              [90, 60, 80],
              [60, 50, 70],
              [30, 40, 70],
              [30, 20, 90]])

ones = np.ones([5, 5])
deviation = A - (ones.dot(A) / len(A))

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def predict(mean1, var1, mean2, var2):
    new_mean = mean1 + mean2
    new_var = var1 + var2
    return [new_mean, new_var]

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def updated_mean(mean1, var1, mean2, var2):
    new_mean = (mean1 * var2 + mean2 * var1) / (var1 + var2)
    return new_mean

def updated_var(var1, var2):
    new_var = 1 / ((1 / var1) + (1 / var2))
    return new_var

jaems33

def expectedReturn(state, action, stateValue):
    # Initiate and populate returns with cost associated with moving cars
    returns = 0.0
    returns -= COST_OF_MOVING * np.absolute(action)
    # Number of cars to start the day
    carsLoc1 = int(min(state[0] - action, MAX_CARS))
    carsLoc2 = int(min(state[1] + action, MAX_CARS))
    # Iterate over Rental Rates
    for rentalsLoc1 in range(0, POISSON_UPPER_BOUND):
        for rentalsLoc2 in range(0, POISSON_UPPER_BOUND):