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This commit is contained in:
Philippe Tillet
2014-10-01 04:44:16 +02:00
parent 3523a3756f
commit 2f6d41f661
2 changed files with 79 additions and 84 deletions
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97
autotune/python/dataset.py
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@@ -17,7 +17,7 @@ def resample(X, tbincount, densities, step):
r = random.random()
while(True):
if(len(tbincount)==0 or len(densities)==0 or r<=1.0/len(densities)):
x = np.array([step*random.randint(1,40), step*random.randint(1,40), step*random.randint(1,40)]);
x = np.array([step*random.randint(1,40), step*random.randint(1,40), step*random.randint(1,40)])
else:
probs = [1.0/x if x>0 else 0 for x in tbincount]
distr = np.random.choice(range(tbincount.size), p = probs/np.sum(probs))
@@ -28,67 +28,46 @@ def resample(X, tbincount, densities, step):
return x.astype(int)
def generate_dataset(TemplateType, execution_handler):
I = 0
I = 10
step = 64
max_size = 4000
path = "./data"
#Tries to resume
try:
X = np.loadtxt(open(os.path.join(path, "X.csv"),"rb"))
t = np.loadtxt(open(os.path.join(path, "t.csv"),"rb"))
profiles = np.loadtxt(open(os.path.join(path, "profiles.csv"),"rb")).tolist()
if not isinstance(profiles[0], list):
profiles = [profiles]
N = t.size
X.resize((N+I, 3), refcheck=False)
t.resize(N+I, refcheck=False)
print 'Resuming dataset generation...'
except:
X = np.empty((I,I))
t = np.empty(I)
profiles = []
N = 0
pass
#Generates new data
print "Getting some good profiles..."
densities = [KernelDensity(kernel='gaussian', bandwidth=2*step).fit(X[t==i,:]) for i in range(int(max(t))+1)] if N else [];
X.resize((N+I, 3), refcheck=False)
t.resize(N+I, refcheck=False)
for i in range(I):
tbincount = np.bincount(t[0:i+1].astype(int))
x = resample(X, tbincount, densities, step)
y = execution_handler(x)
if y not in profiles:
profiles.append(y)
densities.append(KernelDensity(kernel='gaussian', bandwidth=2*step))
idx = profiles.index(y)
X[N+i,:] = x
t[N+i] = idx
densities[idx].fit(X[t[0:N+i+1]==idx,:])
np.savetxt(os.path.join(path,"X.csv"), X)
np.savetxt(os.path.join(path,"t.csv"), t)
np.savetxt(os.path.join(path,"profiles.csv"), profiles)
print "Generating the dataset..."
N = 500
Y = np.empty((N, len(profiles)))
X = np.empty((N,3))
t = []
for i in range(N):
x = resample(X, np.bincount(t), densities, step)
for j,y in enumerate(profiles):
T = execution_handler(x, os.devnull, decode(map(int, y)))
Y[i,j] = 2*1e-9*x[0]*x[1]*x[2]/T
idx = np.argmax(Y[i,:])
X[i,:] = x
t = np.argmax(Y[:i+1,], axis=1)
densities[idx].fit(X[t==idx,:])
np.savetxt(os.path.join(path,"Y.csv"), Y)
# print "Getting some good profiles..."
# X = np.empty((I, 3))
# t = np.empty(I)
# profiles = []
# for i in range(I):
# x = resample(X, [], [], step)
# y = execution_handler(x)
# if y not in profiles:
# profiles.append(y)
# idx = profiles.index(y)
# X[i,:] = x
# t[i] = idx
# densities = [KernelDensity(kernel='gaussian', bandwidth=2*step).fit(X[t==i,:]) for i in range(int(max(t))+1)];
#
# print "Generating the dataset..."
# N = 1000
# Y = np.empty((N, len(profiles)))
# X = np.empty((N,3))
# t = []
#
# for i in range(N):
# x = resample(X, np.bincount(t), densities, step)
# for j,y in enumerate(profiles):
# T = execution_handler(x, os.devnull, decode(map(int, y)))
# Y[i,j] = 2*1e-9*x[0]*x[1]*x[2]/T
# idx = np.argmax(Y[i,:])
# X[i,:] = x
# t = np.argmax(Y[:i+1,], axis=1)
# densities[idx].fit(X[t==idx,:])
#
# np.savetxt(os.path.join(path,"profiles.csv"), profiles)
# np.savetxt(os.path.join(path,"X.csv"), X)
# np.savetxt(os.path.join(path,"Y.csv"), Y)
profiles = np.loadtxt(os.path.join(path,"profiles.csv"))
X = np.loadtxt(os.path.join(path,"X.csv"))
Y = np.loadtxt(os.path.join(path,"Y.csv"))
return X, Y, profiles