def vectorMatrixMulti():
size=8
sigmaSize = 4
v = np.array([(size-x) for x in range(size)])
m1 = matrix_A(size)
result = m1.dot(v)
tikz = mvis.print_tikz_vector(result, "=", True)
tikz += mvis.print_tikz_matrix(m1, "\\times", False)
tikz += mvis.print_tikz_vector(v, "", False)
f = open("tikz-vectormatrix-sparse.pgf", "w")
f.write(mvis.print_tikz_image(tikz))
f.close()
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m1)
print(sell1.m_final)
sell1.print(0,0)
result = scscalc.matrix_times_vector(sell1,v,False)
tikz2, sell1RowPtrs = mvis.print_tikz_scs(sell1, "\\times", False)
tikz = mvis.print_tikz_vector(result, "=", True, sell1RowPtrs)
tikz += tikz2
tikz += mvis.print_tikz_vector(v, "", False)
f = open("tikz-vectormatrix-sparse.pgf", "w")
f.write(mvis.print_tikz_image(tikz))
f.close()
def slicing():
# Dense
size = 5
m1 = np.random.randint(1,9,size=(size,size))
m2 = m1[0:4,0:5]
m3 = m1[0:5,0:4]
tikz = mvis.print_tikz_matrix(m1, "\\rightarrow", True, True, True)
tikz += mvis.print_tikz_matrix(m2,"or", False, True, True)
tikz += mvis.print_tikz_matrix(m3,"", False, True, True)
f = open("tikz-slicing-dense.pgf", "w")
f.write(mvis.print_tikz_image(tikz))
f.close()
# Sparse SCS
size = 9
sigmaSize = 6
m1 = smgen.create(int(size), int(size))
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m1)
tikzb, sell1RowPtrs = mvis.print_tikz_scs(sell1, "\\rightarrow", True)
tikz21, sell1RowPtrs = mvis.print_tikz_scs(sell1,"", False)
# tikzb += tikz2
tikzb += tikz21
f = open("tikz-slicing-scs.pgf", "w")
f.write(mvis.print_tikz_image(tikzb))
f.close()
def random_for_properties():
ms = {}
min = 25
max = 36
amount_of_matrices = 1
timestamp = str(datetime.fromtimestamp(datetime.timestamp(datetime.now()))).replace(" ","_").replace(":","-")
# get global count
glob = 0
for r, d, f in os.walk("./tmp"):
# print("DO",f)
for file in f:
if '.json' in file:
glob +=1
filename = 'tmp/'+str(glob)+'-matrix-examples-'+timestamp+'.json'
for i in range(amount_of_matrices):
# Some Output for better working
print("\n#############################################")
print("This is matrix "+str(i)+" of "+filename+"\n")
ms[i] = {}
ms[i]['x'] = rand.randrange(min, max, 1)
ms[i]['y'] = rand.randrange(min, max, 1)
m = smgen.diag_prob(ms[i]['x'], ms[i]['y'])
ms[i]['highlight'] = {}
while True:
ms[i]['highlight']['x'] = rand.randrange(0, ms[i]['x']-1, 1)
ms[i]['highlight']['y'] = rand.randrange(0, ms[i]['y']-1, 1)
if m[ms[i]['highlight']['y']][ms[i]['highlight']['x']] != 0:
break
ms[i]['matrix'] = m.tolist()
mvis.matprint(m,ms[i]['highlight']['x'],ms[i]['highlight']['y'],None,None,None,True) # Mark Diagonal = true
c = rand.randrange(3, max/4, 3)
ms[i]['sellcsigma'] = {}
ms[i]['sellcsigma']['c'] = 6
ms[i]['sellcsigma']['sigma'] = ms[i]['sellcsigma']['c']*2
ms[i]['sellcsigma']['units'] = rand.randrange(2, 8, 2)
ms[i]['sellcsigma']['pattern'] = "balanced"
# print("###############################################")
# print(ms[i]['sellcsigma'])
sell1 = scs.Sell_c_sigma(ms[i]['sellcsigma']['c'], ms[i]['sellcsigma']['sigma'])
sell1.construct(m)
sell1.prepare_units(ms[i]['sellcsigma']['units'], up.balanced)
sell1.print(ms[i]['highlight']['x'],ms[i]['highlight']['y'])
print("\n")
with open(filename, 'w') as outfile:
json.dump(ms, outfile)
preint("\begin{tikzpicture}[every node/.style={minimum size=0.45cm-\pgflinewidth, outer sep=0pt}, scale=0.5]")
def index_example():
x = 25
y = 30
m = np.zeros((y, x))
for i in range(0,x):
m[i][i] = i
for i in range(x,y):
m[i][5] = i
m[0][0] = 0.1
m[17][0] = 0.1
m[17][1] = 0.1
sell1 = scs.Sell_c_sigma(6,12)
sell1.construct(m)
# sell1.prepare_units(1, up.balanced)
mvis.matprint(m,17,17,None,None,None,True) # Mark Diagonal = true
sell1.print(17,17)
print("\n")
def exampleMMM():
size = 8
sigmaSize = 4
m1=matrix_A(size)
m2 = np.zeros((size,size))
m2[0][0]=1
m2[0][1]=2
m2[0][6]=3
m2[1][1]=4
m2[1][2]=5
m2[2][5]=6
m2[2][6]=7
m2[3][0]=8
m2[3][1]=9
m2[3][6]=10
m2[4][0]=11
m2[4][3]=12
m2[4][4]=13
m2[4][7]=14
m2[5][2]=15
m2[5][5]=16
m2[5][6]=17
m2[6][3]=18
m2[6][6]=19
m2[7][0]=20
m2[7][6]=21
m2[7][7]=22
print(matrixmult(m1,m2))
m2 = m2.transpose()
result = np.zeros((8,8))
# Normal multiplcation
# iterate through rows of
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m1)
print(sell1.m_final)
sell1.print(0,0)
sell2 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
# sell2 = scs.Sell_c_sigma(8, 8)
sell2.construct(m2)
print(sell1.m_final)
sell2.print(0,0)
result = scscalc.matrix_times_matrix(sell1,sell2,True)
mvis.matprint(result,-1,-1,None,None,None,True)
tikz = mvis.print_tikz_matrix(m1,"\\times", True, True, True)
tikz += mvis.print_tikz_matrix(m2,"=", False, True, True)
tikz += mvis.print_tikz_matrix(result, "", False, True, True)
result = scscalc.matrix_times_matrix(sell1,sell2,True)
tikz2, sell1RowPtrs = mvis.print_tikz_scs(sell1, "\\times", True)
tikz2a, sell2RowPtrs = mvis.print_tikz_scs(sell2, "=", False)
tikz2 +=tikz2a
tikz2 += mvis.print_tikz_matrix(result, "", False, True, True, [sell1RowPtrs, sell2RowPtrs])
f = open("tikz-matrix.txt", "w")
f.write(mvis.print_tikz_image(tikz))
f.close()
f = open("tikz-scs.txt", "w")
f.write(mvis.print_tikz_image(tikz2))
f.close()
def quickbenchmark_vector(size = 512, p=True):
sigmaSize = 64 * (size/512)
reps_mat = 1
reps = 1
reps_numpy = 1
results_all= {"manual": [], "numpy":[], "scs": [], "speedup": []}
if p or True:
print("Repetions: "+str(reps_mat)+" x "+str(reps))
print("Matrix: "+str(size)+"x"+str(size))
print("Sell-"+str(int(sigmaSize/2))+"-"+str(sigmaSize))
for rep_over in range (0, reps_mat):
if p:
print()
m1 = smgen.create(int(size), int(size))
v1 = np.random.randint(100, size=size)
allend = 0
allend_numpy = 0
allend_scs=0
for i in range(0, reps):
start = time()
vectormult(m1,v1)
end = time()
allend += end - start
if p:
print("Done Manual "+str(1+i)+"/"+str(reps)+": "+str(end - start))
for i in range(0, reps_numpy ):
start = time()
m1.dot(v1)
end = time()
allend_numpy += end - start
if p:
print("Done Numpy "+str(1+i)+"/"+str(reps)+": "+str(end - start))
#
start = time()
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m1)
end = time()
if p:
print("Prep1: "+ str((end - start)))
#
#
# # tikz = mvis.print_tikz_matrix(m1,"\\times", True, True, True)
# # tikz += mvis.print_tikz_matrix(m2,"=", False, True, True)
# # tikz2a, sell1RowPtrs = mvis.print_tikz_scs(sell1, "\\times", False)
# # tikz2b, sell2RowPtrs = mvis.print_tikz_scs(sell2, "=", False)
# # tikz +=tikz2a
# # tikz +=tikz2b
# # f = open("tikz-scs-bench.pgf", "w")
# # f.write(mvis.print_tikz_image(tikz))
# # f.close()
#
for i in range(0, reps):
start = time()
scscalc.matrix_times_vector(sell1,v1,True)
end = time()
allend_scs += end - start
if p:
print("Done SCS "+str(1+i)+"/"+str(reps)+": "+str(end - start))
if p:
print("\tRESULTS ("+str(rep_over)+")")
print("\tManual: "+ str((allend) / reps))
print("\tnumpy: "+ str((allend_numpy) / reps_numpy))
print("\tSCS: "+ str((allend_scs) / reps))
results_all["manual"].append(allend)
results_all["scs"].append(allend_scs)
results_all["numpy"].append(allend_numpy)
results_all["speedup"].append(allend/allend_scs)
if p:
print("\n\n\tResults Manual:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["manual"][rep_over]))
print("\tResults Numpy:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["numpy"][rep_over]))
print("\tResults SCS:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["scs"][rep_over]))
print("\n\tBest Speedup: "+str(max(results_all["speedup"])))
print("\tWorst Speedup: "+str(min(results_all["speedup"])))
return results_all
def quickbenchmark():
size = 512
sigmaSize = 64
reps_mat = 3
reps = 2
results_all= {"manual": [], "numpy":[], "scs": [], "speedup": []}
print("Repetions: "+str(reps_mat)+" x "+str(reps))
print("Matrix: "+str(size)+"x"+str(size))
print("Sell-"+str(int(sigmaSize/2))+"-"+str(sigmaSize))
for rep_over in range (0, reps_mat):
print()
m1 = smgen.create(int(size), int(size))
m2 = smgen.create(int(size), int(size))
allend = 0
allend_numpy = 0
allend_scs=0
for i in range(0, reps):
start = time()
matrixmult(m1,m2)
end = time()
allend += end - start
print("Done Manual "+str(1+i)+"/"+str(reps)+": "+str(end - start))
for i in range(0, reps):
start = time()
np.matmul(m1,m2)
end = time()
allend_numpy += end - start
print("Done Numpy "+str(1+i)+"/"+str(reps)+": "+str(end - start))
start = time()
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m1)
end = time()
# tikz2, sell1RowPtrs = mvis.print_tikz_scs(sell1, "", True, False)
# f = open("tikz-scs-bench.txt", "w")
# f.write(mvis.print_tikz_image(tikz2))
# f.close()
print("Prep1: "+ str((end - start)))
start = time()
m2 = m2.transpose()
sell2 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell2.construct(m2)
end = time()
print("Prep2: "+ str((end - start)))
# tikz = mvis.print_tikz_matrix(m1,"\\times", True, True, True)
# tikz += mvis.print_tikz_matrix(m2,"=", False, True, True)
# tikz2a, sell1RowPtrs = mvis.print_tikz_scs(sell1, "\\times", False)
# tikz2b, sell2RowPtrs = mvis.print_tikz_scs(sell2, "=", False)
# tikz +=tikz2a
# tikz +=tikz2b
# f = open("tikz-scs-bench.pgf", "w")
# f.write(mvis.print_tikz_image(tikz))
# f.close()
for i in range(0, reps):
start = time()
scscalc.matrix_times_matrix(sell1,sell2,True)
end = time()
allend_scs += end - start
print("Done SCS "+str(1+i)+"/"+str(reps)+": "+str(end - start))
print("\tRESULTS ("+str(rep_over)+")")
print("\tManual: "+ str((allend) / reps))
print("\tnumpy: "+ str((allend_numpy) / reps))
print("\tSCS: "+ str((allend_scs) / reps))
results_all["manual"].append(allend)
results_all["scs"].append(allend_scs)
results_all["numpy"].append(allend_numpy)
results_all["speedup"].append(allend/allend_scs)
print("\n\n\tResults Manual:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["manual"][rep_over]))
print("\tResults Numpy:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["numpy"][rep_over]))
print("\tResults SCS:")
for rep_over in range (0, reps_mat):
print("\t\t"+str(rep_over)+": "+str(results_all["scs"][rep_over]))
print("\n\tBest Speedup: "+str(max(results_all["speedup"])))
print("\tWorst Speedup: "+str(min(results_all["speedup"])))
def main():
# for i in range(12):
size = 12.0
sigmaSize = 6.0
units=2
print("\nTest\n")
m = smgen.create(int(size), int(size))
# Nummerate matrix
i = 1
for irow, row in enumerate(m):
for icol, col in enumerate(row):
if col != 0:
m[irow][icol] = i
i +=1
mvis.matprint(m,13,11,None,None,None,True) # Mark Diagonal = true
# print(m)
r = 0
base = 0
tikz = "\\definecolor{myblue}{rgb}{0, 0.624, 0.89}\n\\definecolor{mypurple}{rgb}{0.576, 0.376, 0.216}\n\\definecolor{mygreen}{rgb}{0.35,0.788,0.333}\n\\definecolor{myred}{rgb}{0.976,0.698,0.2}\n\\definecolor{highlight}{rgb}{0.89, 0.024, 0.075}\n\\begin{tikzpicture}[every node/.style={minimum size=0.95cm-\\pgflinewidth, outer sep=0pt}]\n\t\\draw[step=1cm,color=black] ("+str(base)+",0) grid ("+str(base+size)+","+str(size)+");\n\t\\foreach \\y\\x\\i in {%\n\t\t"
for row in m:
c = 0
v = False
for col in row:
if col != 0:
tikz += str(r)+ "/ " + str(c)+"/"+str(int(col-1))+", "
v = True
c+=1
r+=1
if v:
tikz += "\n\t\t"
tikz = tikz[:-5]+"%"
tikz += "\n\t}{\n\t\t\\node[fill=black] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {};\n\t\\node[color=white] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {$a_{\\i}$};\n\t}\n\n\n"
base += size +1
sell1 = scs.Sell_c_sigma(sigmaSize/2, sigmaSize)
sell1.construct(m)
# sell1.prepare_units(4, up.uniform)
print(sell1.m_final)
maxrow = 0
for isigma, sigma in enumerate(sell1.m_final):
for ichunk, chunk in enumerate(sigma):
maxcol=0
for icol, col in enumerate(chunk[0]):
if col != 0:
maxcol+=1
if maxrow < maxcol:
maxrow = maxcol
tikz += "\t\\draw[step=1cm,color=black] ("+str(base)+","+str(size-(isigma*sigmaSize+ichunk*sigmaSize/2))+") grid ("+str(base+maxcol)+","+str(size-sigmaSize/2-(isigma*sigmaSize+ichunk*sigmaSize/2))+");\n"
tikz += "\t\\foreach \\y\\x\\i in {%\n\t\t"
for isigma, sigma in enumerate(sell1.m_final):
for ichunk, chunk in enumerate(sigma):
for irow, row in enumerate(chunk):
v = False
for icol, col in enumerate(row):
if col != 0:
tikz += (str(isigma*sigmaSize+ichunk*sigmaSize/2+irow)+ "/" +str(icol)+ "/"+str(int(col-1)))
# print(icol, (len(row)-1))
# if isigma != (len(sell1.m_final)-1) and ichunk != (len(sigma)-1) and irow != (len(chunk)-1) and icol != (len(row)-1):
tikz += ", "
v = True
if v:
tikz += "\n\t\t"
tikz = tikz[:-5]+"%"
tikz += "\n\t}{\n\t\t\\node[fill=black] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {};\n\t\\node[color=white] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {$a_{\\i}$};\n\t}\n\n\n"
base += maxrow+1
for isigma, sigma in enumerate(sell1.m_final):
for ichunk, chunk in enumerate(sigma):
maxcol=0
for icol, col in enumerate(chunk[0]):
if col != 0:
maxcol+=1
tikz += "\t\\draw[step=1cm,color=black] ("+str(base)+","+str(size-(isigma*sigmaSize+ichunk*sigmaSize/2))+") grid ("+str(base+maxcol)+","+str(size-sigmaSize/2-(isigma*sigmaSize+ichunk*sigmaSize/2))+");\n"
tikz += "\t\\foreach \\y\\x\\c\\i in {%\n\t\t"
tmp = "\n\t\\foreach \\y\\x\\c\\i in {%\n\t\t"
kcol = 0
chunkcolor = 0
# mem=["","","",""]
# mem[0] = []
# mem[1] = []
# mem[2] = []
# mem[3] = []
for isigma, sigma in enumerate(sell1.m_final):
for ichunk, chunk in enumerate(sigma):
for irow, row in enumerate(chunk):
v = False
kcol = chunkcolor
for icol, col in enumerate(row):
# print(isigma,ichunk,irow,icol,f,k)
# if len(mem[kcol]) < len(row):
# mem[kcol].add("")
if col != 0:
if kcol == 0:
color = "myred"
if kcol == 1:
color = "mygreen"
if kcol == 2:
color = "myblue"
if kcol == 3:
color = "mypurple"
# mem[kcol][icol+irow*] += str(int(col-1))+"/"+color+", "
tikz += (str(isigma*sigmaSize+ichunk*sigmaSize/2+irow)+ "/" +str(icol)+ "/"+color+"/"+str(int(col-1)))
# print(icol, (len(row)-1))
# if isigma != (len(sell1.m_final)-1) and ichunk != (len(sigma)-1) and irow != (len(chunk)-1) and icol != (len(row)-1):
tikz += ", "
v = True
else:
if kcol == 0:
color = "myred"
if kcol == 1:
color = "mygreen"
if kcol == 2:
color = "myblue"
if kcol == 3:
color = "mypurple"
# mem[kcol][icol] += str(0)+"/"+color+", "
tmp += (str(isigma*sigmaSize+ichunk*sigmaSize/2+irow)+ "/" +str(icol)+ "/"+color+"/")
# print(icol, (len(row)-1))
# if isigma != (len(sell1.m_final)-1) and ichunk != (len(sigma)-1) and irow != (len(chunk)-1) and icol != (len(row)-1):
tmp += ", "
# if irow == len(chunk)-1:
# mem[kcol] = mem[kcol][:-2] +";,\n\t\t"
kcol = (kcol + 1) % 4
if v:
tikz += "\n\t\t"
chunkcolor = kcol
# print(mem)
tikz = tikz[:-3] + "%"
tmp = tmp[:-3] + "%"
print(size)
tikz += "\n\t}{\n\t\t\\node[fill=\\c] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {};\n\t\\node[color=black] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {$a_{\\i}$};\n\t}"
tikz += tmp+"\n\t}{\n\t\t\\node[fill=\\c] at (\\x+"+str(base+0.5)+",-\\y+"+str(size-0.5)+") {};\n\t}"
tikz += "\n\\end{tikzpicture}"
f = open("tikz.txt", "w")
f.write(tikz)
f.close()