Merge pull request #75 from Nunigan/master

Multiplikation #3

1 files changed, 25 insertions, 21 deletions

diff --git a/buch/papers/multiplikation/code/MM.py b/buch/papers/multiplikation/code/MM.py
index 47bd6ab..7220ae1 100644
--- a/buch/papers/multiplikation/code/MM.py
+++ b/buch/papers/multiplikation/code/MM.py
@@ -226,28 +226,28 @@ def plot_c_res(ave, num):
 
     MM_t = MM[:,0] 
     MM_n = MM[:,1] 
-    MM_t = np.mean(MM_t.reshape(-1,ave),axis=1)
-    MM_n = np.mean(MM_n.reshape(-1,ave),axis=1)
+    # MM_t = np.mean(MM_t.reshape(-1,ave),axis=1)
+    # MM_n = np.mean(MM_n.reshape(-1,ave),axis=1)
 
     MM_dc_t = MM_dc[:,0] 
     MM_dc_n = MM_dc[:,1] 
-    MM_dc_t = np.mean(MM_dc_t.reshape(-1,ave),axis=1)
-    MM_dc_n = np.mean(MM_dc_n.reshape(-1,ave),axis=1)
+    # MM_dc_t = np.mean(MM_dc_t.reshape(-1,ave),axis=1)
+    # MM_dc_n = np.mean(MM_dc_n.reshape(-1,ave),axis=1)
 
     strassen_t = strassen[:,0] 
     strassen_n = strassen[:,1] 
-    strassen_t = np.mean(strassen_t.reshape(-1,ave),axis=1)
-    strassen_n = np.mean(strassen_n.reshape(-1,ave),axis=1)
+    # strassen_t = np.mean(strassen_t.reshape(-1,ave),axis=1)
+    # strassen_n = np.mean(strassen_n.reshape(-1,ave),axis=1)
     
     winograd_t = winograd[:,0] 
     winograd_n = winograd[:,1] 
-    winograd_t = np.mean(winograd_t.reshape(-1,ave),axis=1)
-    winograd_n = np.mean(winograd_n.reshape(-1,ave),axis=1)
+    # winograd_t = np.mean(winograd_t.reshape(-1,ave),axis=1)
+    # winograd_n = np.mean(winograd_n.reshape(-1,ave),axis=1)
     
     blas_t = blas[:,0] 
     blas_n = blas[:,1] 
-    blas_t = np.mean(blas_t.reshape(-1,ave),axis=1)
-    blas_n = np.mean(blas_n.reshape(-1,ave),axis=1)
+    # blas_t = np.mean(blas_t.reshape(-1,ave),axis=1)
+    # blas_n = np.mean(blas_n.reshape(-1,ave),axis=1)
 
     def func(x, a,b):
         return b*x**a
@@ -261,14 +261,16 @@ def plot_c_res(ave, num):
     plt.rc('axes', labelsize=23)
     plt.rc('xtick', labelsize=23)
     plt.rc('ytick', labelsize=23)
-    plt.plot(MM_n, MM_t, label='3 For Loops', lw=5)
-    plt.plot(winograd_n, winograd_t, label='Winograd MM', lw=5)
-    plt.plot(blas_n, blas_t, label='Blas', lw=5)
-    plt.plot(strassen_n, strassen_t, label='Strassen', lw=5)
-    plt.plot(MM_dc_n, MM_dc_t, label='Divide and Conquer', lw=5)
+    plt.loglog(MM_n, MM_t, label='3 For Loops', lw=5)
+    plt.loglog(winograd_n, winograd_t, label='Winograd MM', lw=5)
+    plt.loglog(blas_n, blas_t, label='Blas', lw=5)
+    plt.loglog(strassen_n, strassen_t, label='Strassen', lw=5)
+    plt.loglog(MM_dc_n, MM_dc_t, label='Divide and Conquer', lw=5)
     plt.xlabel("n")
+    # plt.yscale('log', base=10)
+    # plt.xscale('log', base=2)
     plt.ylabel("time (s)")
-    plt.grid(True)
+    plt.grid(True, which="both", ls="-")
     plt.tight_layout()
     plt.legend(fontsize=19)
     plt.savefig('c_meas_' + str(num)+ '.pdf')    
@@ -278,15 +280,17 @@ def plot_c_res(ave, num):
     # plt.plot(blas_n, func(blas_n, *popt2), 'r-', label='fit MM: a=%5.5f, b=%5.10f' % tuple(popt2))
   
     plt.legend()
-    
+    # return [MM_n,winograd_n,blas_n,strassen_n,MM_dc_n]
+    return [MM_t,winograd_t,blas_t,strassen_t,MM_dc_t]
+
 
 # test%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 if __name__ == '__main__':
-    # plot_c_res(1, 4096)
+    # A = plot_c_res(1, 4096)
 
  
-    # arr = plot(1024)    
-    n = np.logspace(1,12,12,base=2,dtype=(np.int))
+    arr = plot(1024)    
+    # n = np.logspace(1,12,12,base=2,dtype=(np.int))
     # n = np.arange(1,50,2)  
     # A = np.random.randint(-10, 6, (5,3))
     # B = np.random.randint(-10, 6, (3,5))
@@ -297,7 +301,7 @@ if __name__ == '__main__':
     # print(C_test)
     # print(np.equal(C, C_test))
 
-    t_np = test_perfomance(n)
+    # t_np = test_perfomance(n)
     # C = strassen(A, B)
     # C_test = A@B