bs = args["batch_size"]
    else:
        bs = 128

    X_test = args["X_test"]

    preds = iter_test(X_test).tolist()

    new = []
    for pred in preds:
        new.append(np.eye(args["num_classes"])[pred].tolist())
    return new


if __name__ == '__main__':
    x = ArffToArgs()
    x.set_input("data/cpu_act.arff")
    x.set_class_index("last")
    x.set_impute(True)
    x.set_binarize(True)
    x.set_standardize(True)
    x.set_arguments(
        "adaptive=True;alpha=0.01;lambda=0;epochs=500;rmsprop=True")
    args = x.get_args()
    #args["debug"] = True

    args["X_test"] = np.asarray(args["X_train"], dtype="float32")

    model = train(args)

    test(args, model)
            return new
        else:    
            return preds

if __name__ == '__main__':
    x = ArffToArgs()
    #x.set_input("data/auto_price.arff")
    if len(sys.argv) != 3:
        sys.argv.append("data/2dplanes.arff")
        sys.argv.append("kappa")
    x.set_input( sys.argv[1] )
    print "Training on: %s" % sys.argv[1]
    x.set_class_index("last")
    x.set_impute(True)
    x.set_binarize(True)
    x.set_standardize(True)
    if sys.argv[2] == "kappa":
        #x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;rmsprop=True;epochs=5000")
        x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;schedule=500;epochs=5000")
    elif sys.argv[2] == "regression":
        #x.set_arguments("regression=True;alpha=0.1;rmsprop=True;epochs=5000")
        x.set_arguments("regression=True;alpha=0.1;schedule=500;epochs=5000")
    else:
        print "error!"
    args = x.get_args()
    args["debug"] = False

    args["X_test"] = np.asarray(args["X_train"], dtype="float32")

    model = train(args)
    if "batch_size" in args:
        bs = args["batch_size"]
    else:
        bs = 128

    X_test = args["X_test"]

    preds = iter_test(X_test).tolist()

    new = []
    for pred in preds:
        new.append( np.eye(args["num_classes"])[pred].tolist() )
    return new

if __name__ == '__main__':
    x = ArffToArgs()
    x.set_input("data/cpu_act.arff")
    x.set_class_index("last")
    x.set_impute(True)
    x.set_binarize(True)
    x.set_standardize(True)
    x.set_arguments("adaptive=True;alpha=0.01;lambda=0;epochs=500;rmsprop=True")
    args = x.get_args()
    #args["debug"] = True

    args["X_test"] = np.asarray(args["X_train"], dtype="float32")

    model = train(args)

    test(args, model)
Example #4
0

if __name__ == '__main__':
    x = ArffToArgs()
    #x.set_input("data/auto_price.arff")
    if len(sys.argv) != 3:
        sys.argv.append("data/2dplanes.arff")
        sys.argv.append("kappa")
    x.set_input(sys.argv[1])
    print "Training on: %s" % sys.argv[1]
    x.set_class_index("last")
    x.set_impute(True)
    x.set_binarize(True)
    x.set_standardize(True)
    if sys.argv[2] == "kappa":
        #x.set_arguments("expectation=True;a=1;b=0;logistic=True;alpha=0.1;rmsprop=True;epochs=5000")
        x.set_arguments(
            "expectation=True;a=1;b=0;logistic=True;alpha=0.1;schedule=500;epochs=5000"
        )
    elif sys.argv[2] == "regression":
        #x.set_arguments("regression=True;alpha=0.1;rmsprop=True;epochs=5000")
        x.set_arguments("regression=True;alpha=0.1;schedule=500;epochs=5000")
    else:
        print "error!"
    args = x.get_args()
    args["debug"] = False

    args["X_test"] = np.asarray(args["X_train"], dtype="float32")

    model = train(args)