def learn(model):
print("Loading feature data...")
positive = np.load(corpus_path("patterns.npy"))
negative = np.load(corpus_path("nopatterns.npy"))
validate = np.load(corpus_path("validation.npy"))
print("Fitting model...")
model.train(positive, negative, validate)
def score(model):
print("Computing scores on examples...")
with open(corpus_path("manifest.yaml")) as f:
manifest = yaml.load(f)
scores = []
for cls in manifest.keys():
for img in sorted(manifest[cls].keys()):
path = corpus_path(manifest[cls][img]["path"])
im = io.imread(path, as_grey=True)
x = feature_stats(im)
score = float(model.score(np.array([x])))
scores.append((score, path, cls))
return scores
def main():
print("Select model:")
for i in range(len(models)):
print("[{0}] {1}".format(i, models[i][0]))
m = int(input("> "))
if not 0 <= m < len(models):
print("Invalid selection.")
return
if m != 0:
model = models[m][1]()
learn(model)
scores = score(model)
plot(scores, models[m][0])
else:
trained_models = []
for m in models[1:]:
print("Training", m[0])
model = m[1]()
learn(model)
trained_models.append(model)
path = corpus_path("trained_models.p")
print("Saving trained models to", path)
with open(path, "wb") as f:
pickle.dump(trained_models, f)
def preload_models():
global trained_models
with open(corpus_path("trained_models.p"), "rb") as f:
trained_models = pickle.load(f)
