def test_sum_mean_std_var(self):
for fn in [ch.sum, ch.mean,ch.var, ch.std]:
# Create fake input and differences in input space
data1 = ch.ones((3,4,7,2))
data2 = ch.array(data1.r + .1 * np.random.rand(data1.size).reshape(data1.shape))
diff = data2.r - data1.r
# Compute outputs
result1 = fn(data1, axis=2)
result2 = fn(data2, axis=2)
# Empirical and predicted derivatives
gt = result2.r - result1.r
pred = result1.dr_wrt(data1).dot(diff.ravel()).reshape(gt.shape)
#print np.max(np.abs(gt - pred))
if fn in [ch.std, ch.var]:
self.assertTrue(1e-2 > np.max(np.abs(gt - pred)))
else:
self.assertTrue(1e-14 > np.max(np.abs(gt - pred)))
# test caching
dr0 = result1.dr_wrt(data1)
data1[:] = np.random.randn(data1.size).reshape(data1.shape)
self.assertTrue(result1.dr_wrt(data1) is dr0) # changing values shouldn't force recompute
result1.axis=1
self.assertTrue(result1.dr_wrt(data1) is not dr0)
self.assertEqual(ch.mean(ch.eye(3),axis=1).ndim, np.mean(np.eye(3),axis=1).ndim)
self.assertEqual(ch.mean(ch.eye(3),axis=0).ndim, np.mean(np.eye(3),axis=0).ndim)
self.assertEqual(ch.sum(ch.eye(3),axis=1).ndim, np.sum(np.eye(3),axis=1).ndim)
self.assertEqual(ch.sum(ch.eye(3),axis=0).ndim, np.sum(np.eye(3),axis=0).ndim)
def test_sum_mean_std_var(self):
for fn in [ch.sum, ch.mean, ch.var, ch.std]:
# Create fake input and differences in input space
data1 = ch.ones((3,4,7,2))
data2 = ch.array(data1.r + .1 * np.random.rand(data1.size).reshape(data1.shape))
diff = data2.r - data1.r
# Compute outputs
result1 = fn(data1, axis=2)
result2 = fn(data2, axis=2)
# Empirical and predicted derivatives
gt = result2.r - result1.r
pred = result1.dr_wrt(data1).dot(diff.ravel()).reshape(gt.shape)
#print np.max(np.abs(gt - pred))
if fn in [ch.std, ch.var]:
self.assertTrue(1e-2 > np.max(np.abs(gt - pred)))
else:
self.assertTrue(1e-14 > np.max(np.abs(gt - pred)))
# test caching
dr0 = result1.dr_wrt(data1)
data1[:] = np.random.randn(data1.size).reshape(data1.shape)
self.assertTrue(result1.dr_wrt(data1) is dr0) # changing values shouldn't force recompute
result1.axis=1
self.assertTrue(result1.dr_wrt(data1) is not dr0)
self.assertEqual(ch.mean(ch.eye(3),axis=1).ndim, np.mean(np.eye(3),axis=1).ndim)
self.assertEqual(ch.mean(ch.eye(3),axis=0).ndim, np.mean(np.eye(3),axis=0).ndim)
self.assertEqual(ch.sum(ch.eye(3),axis=1).ndim, np.sum(np.eye(3),axis=1).ndim)
self.assertEqual(ch.sum(ch.eye(3),axis=0).ndim, np.sum(np.eye(3),axis=0).ndim)
def lstsq(a, b, rcond=-1):
if rcond != -1:
raise Exception('non-default rcond not yet implemented')
x = Ch(lambda a, b : pinv(a).dot(b))
x.a = a
x.b = b
residuals = ch.sum( (x.a.dot(x) - x.b) **2 , axis=0)
rank = NotImplementedError
s = NotImplementedError
return x, residuals, rank, s
def norm(x, ord=None, axis=None):
if ord is not None or axis is not None:
raise NotImplementedError("'ord' and 'axis' should be None for now.")
return ch.sqrt(ch.sum(x**2))