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#Install package import sys !{sys.executable} -m pip install numpy !{sys.executable} -m pip install matplotlib # import packages import numpy as np import scipy from scipy import optimize from scipy.optimize import minimize Deterministic model Time scale: the decisions are taken every hour during the day.

Project Logistic regression #Install package import sys !{sys.executable} -m pip install numpy !{sys.executable} -m pip install matplotlib !{sys.executable} -m pip install scikit-learn import numpy as np import matplotlib.pyplot as plt from time import time import random import sklearn 1) Device Type: Choose a device type (Lights, Camera, Smart Speaker, Thermostat,…) and plot the data

TP 4 Logistic regression import sys !{sys.executable} -m pip install numdifftools !{sys.executable} -m pip install scikit-learn import numpy as np import matplotlib.pyplot as plt from time import time import random #params = {'tex.

#Install package import sys !{sys.executable} -m pip install numpy !{sys.executable} -m pip install matplotlib # import packages import numpy as np import scipy from scipy import optimize from scipy.optimize import minimize Deterministic model Time scale: the decisions are taken every hour during the day.

#Install package import sys !{sys.executable} -m pip install numpy !{sys.executable} -m pip install matplotlib # import packages import numpy as np import scipy from scipy import optimize from scipy.optimize import minimize Deterministic model Time scale: the decisions are taken every hour during the day.

TP 4 Logistic regression import numpy as np import matplotlib.pyplot as plt from time import time import random #params = {'tex.usetex': True} We are given a family of $n$ points $x_i$ in $\mathbb{R}^2$, as well as $n$ \textit{labels} associated $(y_i)_{1 \leq i \leq n}$ in ${0,1}$.

data; #incoming flow param :trange: Q:= 1 386 2 346 3 416 4 713 5 1532 6 2000 7 1982 8 1431 9 780 10 476 11 450 12 420 ; # revenue param B:= 1 3500 2 700 3 2250 4 500 ; param d : 1 2 3 4 5 6 7 8 9 10 11 12:= 1 3 3 3 2 4 5 5 4 5 0 0 0 2 0 0 0 1 3 3 0 0 0.

data; param:nrange :t:= 1 1790 2 1800 3 1810 4 1820 5 1830 6 1840 7 1850 8 1860 9 1870 10 1880 11 1890 12 1900 13 1910 14 1920 15 1930 16 1940 17 1950 18 1960 19 1970 20 1980 21 1990 22 2000; param P:= 1 3.

Gradient descent with projection and augmented lagrangian algorithm To find the infimum of an arbitrary cost function, we use here the gradient descent. Let us consider the function $f(x,y)=0.5(x^2+(y-1)^2)$ that we aim at minimizing on $K\subset \mathbb{R}^2$ with $K={(x,y) | x= y}$.