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Add training code

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This commit is contained in:
Ralph Tang
2018-02-04 21:39:25 -05:00
parent 579d187d8e
commit 5df6123aba
4 changed files with 150 additions and 27 deletions
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vdpwi/utils/preprocess.py
+8 -8
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@@ -35,20 +35,20 @@ def discrete_tnorm(a, b, tgt_loc, sigma=1, n_steps=100):
x = tgt_loc - sigma * tgt_loc_update(x)
tn = truncnorm((a - x) / sigma, (b - x) / sigma, loc=x, scale=sigma)
rrange = np.arange(a, b + 1)
pdf = tn.pdf(rrange)
pdf /= np.sum(pdf)
return pdf
pmf = tn.pdf(rrange)
pmf /= np.sum(pmf)
return pmf
def discrete_lerp(a, b, ground_truth):
pdf = np.zeros(b - a + 1)
pmf = np.zeros(b - a + 1)
c = int(np.ceil(ground_truth + 1E-8))
f = int(np.floor(ground_truth))
pdf[min(c - a, b - a)] = ground_truth - f
pdf[f - a] = c - ground_truth
return pdf
pmf[min(c - a, b - a)] = ground_truth - f
pmf[f - a] = c - ground_truth
return pmf
def smoothed_labels(truth, n_labels):
return discrete_tnorm(1, n_labels, truth, sigma=0.35, n_steps=0)
return discrete_lerp(1, n_labels, truth)
def preprocess(filename, output_name="sim_sparse.txt"):
print("Preprocessing {}...".format(filename))