57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LinearRegression
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import numpy as np
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Y = [661, 971, 1282, 1613, 1936, 2273, 2609, 2933, 3264]
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Y2 = [205, 290, 395, 478, 562, 625, 718, 808, 899]
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Y_ARM = [424, 824 , 1224 , 1624, 2024, 2424 , 2824, 3224, 3624]
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Y_gcc = [2296, 5119, 6715, 9078, 10830, 12541, 15041, 16780, 18883]
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Y_O3 = [165, 287, 380, 559, 900, 901, 776, 1122, 1574]
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X = []
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temps = []
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for i in range(9) :
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Y[i] = Y[i]*3/100
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Y2[i]=Y[i]*3/100
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Y_ARM[i] = Y_ARM[i]*3/100
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Y_gcc[i] = Y_gcc[i]*3/100/4
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Y_O3[i] = Y_O3[i]*3/100/4
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X.append(50*(1+i))
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temps.append(Y[i]/33333334)
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plt.scatter(X, Y, color="b", marker="x", label="FPGA 50 MHz")
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plt.scatter(X, Y2, color="g", marker="x", label="FPGA 200 MHz")
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plt.scatter(X, Y_ARM, color="g", label="mesures ARM")
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plt.scatter(X, Y_gcc, color="y",marker="*", label="desktop non opti")
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plt.scatter(X, Y_O3, color="pink", marker="*",label="desktop_opti")
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x = np.array(X).reshape(-1, 1)
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y = np.array(Y).reshape(-1, 1)
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y2 = np.array(Y2).reshape(-1, 1)
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reg = LinearRegression().fit(x, y)
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reg2 = LinearRegression().fit(x, y2)
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print("score obtenu : " + str(reg.score(x, y)))
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print("score obtenu : " + str(reg.score(x, y2)))
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print("attente à zéro : {}".format(reg.intercept_))
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print("attente à zéro : {}".format(reg2.intercept_))
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x_lin = [0, max(X)]
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y_lin = [reg.predict(np.array([0]).reshape(-1, 1)), reg.predict(np.array([x_lin[1]]).reshape(-1, 1))]
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y_lin2 = [reg2.predict(np.array([0]).reshape(-1, 1)), reg2.predict(np.array([x_lin[1]]).reshape(-1, 1))]
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y_lin = [y_lin[0][0][0], y_lin[1][0][0]]
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y_lin2 = [y_lin2[0][0][0], y_lin2[1][0][0]]
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plt.plot(x_lin, y_lin, color = "r", label="RegLin 50 score : {:.4f}".format(reg.score(x, y)))
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plt.plot(x_lin, y_lin2, color = "r")
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plt.xlim([0, 500])
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plt.ylim([0, 100])
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plt.legend()
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plt.title("Temps d'exécution en fonction de n_max")
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plt.ylabel("T (0.1 µs)")
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plt.xlabel("N_max")
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plt.savefig("M2_SETI/A2/fibonacci/linéaire.png")
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plt.show()
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