def check_boxes(envir):
boxes = envir.boxes
for box_i in boxes:
for box_j in boxes:
if (box_i != box_j):
plot.plot_box([box_i, box_j])
box_coll = (box_i, box_j, box_i(box_j), box_j(box_i))
print("%s;%s;%d,%d" % box_coll)
if str(feat_val)[-3:] == "inf" or str(feat_val) == "nan" or (abs(feat_val)>18 and i==1) :
print feat_val,label
continue
pool_feature.append(feat_val)
#apply pooling operator here
pool_feature = np.std(pool_feature)
if label == -1:
class_1_feature.append(pool_feature )
elif label == 1:
class1_feature.append(pool_feature )
axis = [0, 110, min(min(class_1_feature),min(class1_feature))-1, max(max(class_1_feature),max(class1_feature))+1]
print len(class_1_feature)
print len(class1_feature)
# plot_scatter(class_1_feature, "class-1-"+feat_id_to_name[i],axis)
# plot_scatter(class1_feature, "class1-"+feat_id_to_name[i],axis)
#
# plot_histogram(class_1_feature, "class-1-"+feat_id_to_name[i])
# plot_histogram(class1_feature, "class1-"+feat_id_to_name[i])
data_to_plot = [class1_feature,class_1_feature]
plot_box(data_to_plot, "std-"+feat_id_to_name[i],["class 1","class -1"])
# plot_xy_histogram(class1_feature,class_1_feature, feat_id_to_name[i],["class1-","class-1-"])
class_positive_feature.append(np.std(pool_feature))
else:
class_neutral_feature.append(np.std(pool_feature))
result.append(np.std(pool_feature))
results.append(result)
axis = [
0, 100000,
min(min(class_negative_feature), min(class_positive_feature)) - 1,
max(max(class_negative_feature), max(class_positive_feature)) + 1
]
# plot_scatter(class_1_feature, "class-1-"+feat_id_to_name[i],axis)
# plot_scatter(class1_feature, "class1-"+feat_id_to_name[i],axis)
#
# plot_histogram(class_1_feature, "class-1-"+feat_id_to_name[i])
# plot_histogram(class1_feature, "class1-"+feat_id_to_name[i])
data_to_plot = [
class_positive_feature, class_negative_feature, class_neutral_feature
]
plot_box(data_to_plot, "OKAO", feature_mapping[i],
["class 1", "class -1", "class 0"])
#plot_xy_histogram(class1_feature, class_1_feature, feat_id_to_name[i], ["image/class1-", "image/class-1-"])
labels.append("OKAO-" + feature_mapping[i])
results = list(map(list, zip(*results)))
results = [[]] + results
results[0] = labels
with open("output-OKAO.csv", "wb") as f:
writer = csv.writer(f)
writer.writerows(results)
if str(feat_val)[-3:] == "inf" or str(feat_val) == "nan" or (
abs(feat_val) > 18 and i == 1):
print feat_val, label
continue
pool_feature.append(feat_val)
if label == -1:
class_1_feature.append(np.std(pool_feature))
elif label == 1:
class1_feature.append(np.std(pool_feature))
axis = [
0, 100000,
min(min(class_1_feature), min(class1_feature)) - 1,
max(max(class_1_feature), max(class1_feature)) + 1
]
print len(class_1_feature)
print len(class1_feature)
# plot_scatter(class_1_feature, "class-1-"+feat_id_to_name[i],axis)
# plot_scatter(class1_feature, "class1-"+feat_id_to_name[i],axis)
#
# plot_histogram(class_1_feature, "class-1-"+feat_id_to_name[i])
# plot_histogram(class1_feature, "class1-"+feat_id_to_name[i])
data_to_plot = [class1_feature, class_1_feature]
plot_box(data_to_plot, "ACOUSTIC", feat_id_to_name[i],
["class 1", "class -1"])
# plot_xy_histogram(class1_feature,class_1_feature, feat_id_to_name[i],["class1-","class-1-"])
# continue
if label == -1:
class_1_feature += feat_val
elif label == 1:
class1_feature += feat_val
elif label == 0:
class0_feature += feat_val
anova += feat_val
labels.append(label)
#word cloud 2
# word_cloud(' '.join(class_1_feature), "class_-1")
# word_cloud(' '.join(class1_feature), "class_1")
print len(class1_feature), len(
class_1_feature) #, "\n", class1_feature, "\n", class_1_feature
print "Avg", sum(class1_feature) * 1.0 / len(class1_feature), sum(
class_1_feature) * 1.0 / len(class_1_feature)
print "Max", max(class1_feature), max(class_1_feature)
print "index", class1_feature.index(
max(class1_feature)), class_1_feature.index(max(class_1_feature))
print "Std", np.std(class1_feature), np.std(class_1_feature)
data_to_plot = [class1_feature, class_1_feature, class0_feature]
plot_box(data_to_plot, "std-elongation", ["class 1", "class -1", "class 0"])
#
# plot_xy_histogram(class1_feature,class_1_feature, "std-elongation",["class1-","class-1-"])
print anova
print labels
