# Loading data
print('Loading data...')
X = np.loadtxt(args["fluorescence"], delimiter=",")
X = np.asfortranarray(X, dtype=np.float32)
# pos = np.loadtxt(args["position"], delimiter=",")
# Should we remove some neurons?
if "killing" in args:
if args["network"] in ["normal-3", "normal-4"]:
X = kill(X, args["network"], args["killing"])
else:
raise ValueError("No killing specified for %s" % args["network"])
# Producing the prediction matrix
if args["method"] == 'tuned':
y_pca = make_tuned_inference(X)
else:
y_pca = make_simple_inference(X)
if args["directivity"]:
print('Using information about directivity...')
y_directivity = make_prediction_directivity(X)
# Perform stacking
score = 0.997 * y_pca + 0.003 * y_directivity
else:
score = y_pca
# Save data
if "output_dir" in args:
if not os.path.exists(args["output_dir"]):
os.makedirs(args["output_dir"])
print('Loading data...')
X = np.loadtxt(args["fluorescence"], delimiter=",")
X = np.asfortranarray(X, dtype=np.float32)
# pos = np.loadtxt(args["position"], delimiter=",")
# Should we remove some neurons?
if "killing" in args:
if args["network"] in ["normal-3",
"normal-4"]:
X = kill(X, args["network"], args["killing"])
else:
raise ValueError("No killing specified for %s" % args["network"])
# Producing the prediction matrix
if args["method"] == 'tuned':
y_pca = make_tuned_inference(X)
else:
y_pca = make_simple_inference(X)
if args["directivity"]:
print('Using information about directivity...')
y_directivity = make_prediction_directivity(X)
# Perform stacking
score = 0.997 * y_pca + 0.003 * y_directivity
else:
score = y_pca
# Save data
if "output_dir" in args:
if not os.path.exists(args["output_dir"]):
os.makedirs(args["output_dir"])