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

Y_B2 = [36,  3876, 7716, 11556, 15396, 19236, 23076, 26916, 30756]
Y_B3 = [97, 36786, 84655, 135454, 191580,   247876, 304307, 365937, 427737]
YA_B2 = [28, 2124, 4199, 6345, 8380, 10441, 12525, 14607, 16690]
YA_B3 = [36, 4106 , 8161 , 12071, 16060, 20056 , 24096,  28115, 32068]
X = [0, 128, 256, 384, 512, 640, 768, 896, 1024]

plt.scatter(X, Y_B2, color="b", label="RISCV Branchement 1/2")
plt.scatter(X, Y_B3, color="b", marker="x", label="Branchement 1/3")
plt.scatter(X, YA_B2, color="r", label="ARM Branchement 1/2")
plt.scatter(X, YA_B3, color="r",marker="x", label="ARM Branchement 1/3")

x = np.array(X).reshape(-1, 1)
y = np.array(Y_B2).reshape(-1, 1)
y2 = np.array(YA_B2).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(reg2.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 = "b", label="RegLin score : {:.4f}".format(reg.score(x, y)))
plt.plot(x_lin, y_lin2, color = "r", label="RegLin score : {:.4f}".format(reg2.score(x, y2)))


plt.xlim([0, 1024])
plt.ylim([50, 450000])
plt.legend()
plt.title("Cycles d'exécution en fonction de n_max")
plt.ylabel("Cycles")
plt.xlabel("N_max")
plt.savefig("memory_cycles.png")
plt.show()