import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import numpy as np

Y = [661, 971, 1282, 1613, 1936, 2273, 2609, 2933, 3264]
Y2 = [205, 290, 395, 478, 562, 625, 718, 808, 899]
Y_ARM = [424, 824 , 1224 , 1624, 2024, 2424 , 2824,  3224, 3624]
Y_gcc = [2296, 5119, 6715, 9078, 10830, 12541, 15041, 16780, 18883]
Y_O3 = [165, 287, 380, 559, 900, 901, 776, 1122, 1574]



X = []
temps = []
for i in range(9) :
    Y[i] = Y[i]*3/100
    Y2[i]=Y[i]*3/100
    Y_ARM[i] = Y_ARM[i]*3/100
    Y_gcc[i] = Y_gcc[i]*3/100/4
    Y_O3[i] = Y_O3[i]*3/100/4
    X.append(50*(1+i))
    temps.append(Y[i]/33333334)

plt.scatter(X, Y, color="b", marker="x", label="FPGA 50 MHz")
plt.scatter(X, Y2, color="g", marker="x", label="FPGA 200 MHz")
plt.scatter(X, Y_ARM, color="g", label="mesures ARM")
plt.scatter(X, Y_gcc, color="y",marker="*", label="desktop non opti")
plt.scatter(X, Y_O3, color="pink", marker="*",label="desktop_opti")

x = np.array(X).reshape(-1, 1)
y = np.array(Y).reshape(-1, 1)
y2 = np.array(Y2).reshape(-1, 1)
reg = LinearRegression().fit(x, y)
reg2 = LinearRegression().fit(x, y2)
print("score obtenu : " + str(reg.score(x, y)))
print("score obtenu : " + str(reg.score(x, y2)))
print("attente à zéro : {}".format(reg.intercept_))
print("attente à zéro : {}".format(reg2.intercept_))

x_lin = [0, max(X)]
y_lin = [reg.predict(np.array([0]).reshape(-1, 1)), reg.predict(np.array([x_lin[1]]).reshape(-1, 1))]
y_lin2 = [reg2.predict(np.array([0]).reshape(-1, 1)), reg2.predict(np.array([x_lin[1]]).reshape(-1, 1))]
y_lin = [y_lin[0][0][0], y_lin[1][0][0]]
y_lin2 = [y_lin2[0][0][0], y_lin2[1][0][0]]

plt.plot(x_lin, y_lin, color = "r", label="RegLin 50 score : {:.4f}".format(reg.score(x, y)))
plt.plot(x_lin, y_lin2, color = "r")


plt.xlim([0, 500])
plt.ylim([0, 100])
plt.legend()
plt.title("Temps d'exécution en fonction de n_max")
plt.ylabel("T (0.1 µs)")
plt.xlabel("N_max")
plt.savefig("M2_SETI/A2/fibonacci/linéaire.png")
plt.show()