kayhman

import matplotlib.pyplot as plt
import numpy as np

from forward_autodiff import DualFloat

trip_distance = DualFloat(6.0) # km
trip_duration = DualFloat(12.0) # minutes
trip_avg_speed = DualFloat(30.0) # km/h

# trip duration in minutes

kayhman

from functools import reduce
import numpy as np
import jax.numpy as jnp


class LagrangianPolynome:
    def __init__(self, Ts, Xs):
        self.Ts = Ts
        self.Xs = Xs

kayhman

import numpy as np
import matplotlib.pyplot as plt

from lagrangian_polynomial import LagrangianPolynome

Ts = [0.0, 1.0, 2.0, 3.0]
Xs = [0.0, 1.0, 1.0, 0.0]

poly = LagrangianPolynome(Ts, Xs)

kayhman

from jax import jacfwd

def local_state(f, t):
    traj_pos = f(t)
    traj_speed_f = jacrev(f)
    traj_speed = traj_speed_f(t)
    traj_acc = jacfwd(traj_speed_f)(t)
    return t, traj_pos, traj_speed, traj_acc

kayhman

import numpy as np
from jax import jacfwd, jacrev

from lagrangian_polynomial import LagrangianPolynome


def candidate_trajectory(Ts, Xs, t):
    poly = LagrangianPolynome(Ts, Xs)
    return poly.eval(t)

kayhman

import scipy.integrate as integrate
import numpy as np
import jax.numpy as jnp
from jax import jacfwd, jacrev, jit

from lagrangian_polynomial import LagrangianPolynome


def local_state(f, t):
    traj_pos = f(t)

kayhman

import matplotlib.pyplot as plt
import jax.numpy as jnp
import numpy as np
import jax
from jax import grad, jacfwd, jacrev, jit

from scipy.optimize import minimize, root

from lagrangian_polynomial import LagrangianPolynome
from integration import integrate, integrate_num

kayhman

import matplotlib.pyplot as plt
import jax.numpy as jnp
import numpy as np
from jax import grad, jacfwd, jacrev, jit

from lagrangian_polynomial import LagrangianPolynome
from integration import integrate
from scipy.optimize import minimize, root

A = -9.81

kayhman

import matplotlib.pyplot as plt
from xgboost import XGBRegressor
import numpy as np
import pandas as pd

# Create an XGBoost model
model = XGBRegressor(n_estimators=250)

# Create time serie timestamp indices
ts = np.linspace(0, 10, 100)

kayhman

class DecisionNode:
    """
    Node decision class.
    This is a simple binary node, with potentially two children: left and right
    Left node is returned when condition is true
    False node is returned when condition is false
    """
    def __init__(self, name, condition, value=None):
        self.name = name