def get_encoded_codes(fullset):
result = []
for row in fullset:
disaster_prob_vec = row[:common.N_DISASTER]
categorized_vec = common.categorize(disaster_prob_vec, 0.4, 0.6)
result.append('"' + ''.join(map(str, categorized_vec)) + '"')
return result
def get_clust_samples(fullset):
result = []
for row in fullset:
disaster_prob_vec = row[:common.N_DISASTER]
categorized_vec = common.categorize(disaster_prob_vec, 0.4, 0.6)
if common.is_certain(categorized_vec) and sum(categorized_vec) != 0:
result.append(categorized_vec)
return np.array(result)
def check_and_decide(disaster_prob_vec, probs):
categorized_vec = common.categorize(disaster_prob_vec,
0.4,
0.6,
nodata_to_uncertain=True)
possible_vecs = span(np.array(categorized_vec))
possible_labels = list(map(common.classify, possible_vecs))
decrease_idx = np.argsort(-np.array(probs))
for i, idx in enumerate(decrease_idx):
if idx in possible_labels:
return idx, i
print('error')
def get_uncertain_count_map(fullset):
result = []
for row in fullset:
disaster_prob_vec = row[:common.N_DISASTER]
feature_vec = row[common.N_DISASTER:]
categorized_vec = np.array(common.categorize(disaster_prob_vec, 0.4, 0.6))
if np.count_nonzero(categorized_vec == 9) == 5:
result.append(-99)
else:
result.append(np.count_nonzero(np.logical_or(categorized_vec == 1, categorized_vec == 0)))
return np.array(result)
def get_codes(fullset):
results = []
for i, row in enumerate(fullset):
disaster_prob_vec = row[:common.N_DISASTER]
feature_vec = row[common.N_DISASTER:]
categorized_vec = common.categorize(disaster_prob_vec, 0.4, 0.6)
if common.is_certain(categorized_vec):
label = common.classify(categorized_vec)
results.append(label)
elif -99 in feature_vec:
results.append(-99)
else:
results.append(common.N_CLASS)
return np.array(results)
def get_types(fullset):
results = []
for row in fullset:
disaster_prob_vec = row[:common.N_DISASTER]
feature_vec = row[common.N_DISASTER:]
categorized_vec = common.categorize(disaster_prob_vec, 0.4, 0.6)
if common.is_certain(categorized_vec):
label = common.classify(categorized_vec)
results.append(label)
elif -99 in feature_vec:
results.append(-99)
else:
results.append(UNCERTAIN_LABEL)
return np.array(results)
def get_trainset(fullset, upward=False, with_id=False):
result = []
if upward:
fullset = np.flipud(fullset.reshape(common.N_ROWS, common.N_COLS, -1)).reshape(-1, fullset.shape[-1])
for row in fullset:
disaster_prob_vec = row[:common.N_DISASTER]
feature_vec = row[common.N_DISASTER:]
categorized_vec = common.categorize(disaster_prob_vec, 0.4, 0.6)
if common.is_certain(categorized_vec) and -99 not in feature_vec:
label = common.classify(categorized_vec)
result.append(feature_vec.tolist() + [label])
if with_id:
return np.concatenate((np.arange(len(result))[:, np.newaxis], result), axis=1)
else:
return np.array(result)
