matlab vs python: 跑循环的速度对比
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测试1
matlab代码
N = 20:25;iters = 2.^N;time = zeros(1,length(N));a = 0.111;b = 0.222;for k = 1:length(N) r = 0; t1 = clock; for i = 1:2^N(k) r = 0.5*a + 0.6*b; end t2 = clock; time(k) = etime(t2,t1); endplot(iters, time)xlabel('iter')ylabel('time(/s)') python代码
N = range(20,26)iters = [2**n for n in N]ts = []a, b = 0.111, 0.222for n in N: t1 = time.time() for i in range(2**n): r = 0.5*a + 0.6*b t2 = time.time() ts.append(t2-t1)_, ax = plt.subplots()ax.plot(iters, ts)ax.set_xlabel('iter')ax.set_ylabel('time(/s)') 结果对比
将两者数据画到一起，方便对比。 结论：随着循环增多，两者消耗时间都线性增大。对于这个测试案例（两个乘法和一个加法）。python约比matlab慢60倍
测试2
matlab代码
N = 20:25;iters = 2.^N;time = zeros(1,length(N));a = 0.111;b = 0.222;M = [0.111,0.222;0.111,0.222];for k = 1:length(N) r = 0; t1 = clock; for i = 1:2^N(k) r = M(1,1)*a + M(1,2)*b; end t2 = clock; time(k) = etime(t2,t1); endfigure;plot(iters, time)xlabel('iter')ylabel('time(/s)') python代码
N = range(20,26)iters = [2**n for n in N]ts = []M = np.array([[0.111, 0.222],[0.111, 0.222]])a, b = 0.111, 0.222for n in N: t1 = time.time() for i in range(2**n): r = M[0,0]*a + M[0,1]*b t2 = time.time() ts.append(t2-t1)_, ax = plt.subplots()ax.plot(iters, ts)ax.set_xlabel('iter')ax.set_ylabel('time(/s)') 结果对比
将两者数据画到一起，方便对比。 结论：- 随着循环增多，两者消耗时间都线性增大。python约比matlab慢110倍
- 将此测试结果与测试1对比, 可猜想：仅仅是在2*2矩阵中索引一个数，python也要比matlab很多倍，猜想慢110-60=50倍。再通过一个测试3来验证下猜想。
测试3
matlab代码
N = 20:25;iters = 2.^N;time = zeros(1,length(N));a = 0.111;b = 0.222;M = [0.111,0.222;0.111,0.222];for k = 1:length(N) r = 0; t1 = clock; for i = 1:2^N(k) r = M(1,1); end t2 = clock; time(k) = etime(t2,t1); endfigure;plot(iters, time)xlabel('iter')ylabel('time(/s)') python代码
N = range(20,26)iters = [2**n for n in N]ts = []M = np.array([[0.111, 0.222],[0.111, 0.222]])a, b = 0.111, 0.222for n in N: t1 = time.time() for i in range(2**n): r = M[0,0] t2 = time.time() ts.append(t2-t1)_, ax = plt.subplots()ax.plot(iters, ts)ax.set_xlabel('iter')ax.set_ylabel('time(/s)') 结果对比
猜想正确，仅仅是2*2矩阵索引一个数，python也比matlab慢50倍。
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