def get_point(ys): xs = range(len(ys)) ys = ys['Adj. Close'] result = (0, -99999) for i in xs[CHECK_PERIOD:-CHECK_PERIOD]: before_line = linear_regression.train(xs[i - CHECK_PERIOD:i], ys[i - CHECK_PERIOD:i]) after_line = linear_regression.train(xs[i:i + CHECK_PERIOD], ys[i:i + CHECK_PERIOD]) fracture = after_line[0]**2 / before_line[0]**2 if fracture > result[1]: result = i, fracture, before_line, after_line if result[0]: return result else: return None
def train_function(x, y, model, window_state): if not model: class Model: w = np.zeros((x.shape[1] + 1, 1)) sum_error = 0 i = 0 model = Model() model.w = train(x, y, self.draw) self.w = model.w error = evaluate_error(x, y, model.w) if self.output: print "Error: ", error model.sum_error += error model.i += 1 return model
def main(): x = [] valid = [] test = [] for i in xrange(2, 68, 5): print "Current training set size: ", i shutil.rmtree(TRAIN_DIR, ignore_errors=True) shutil.rmtree(TEST_DIR, ignore_errors=True) shutil.rmtree(VALIDATION_DIR, ignore_errors=True) split_data.split_data(i) theta = linear_regression.train() x.append(i) valid.append(linear_regression.validation(theta)) test.append(linear_regression.eval(theta)) plt.plot(x, valid) plt.plot(x, test) plt.ylabel('Accuracy') plt.xlabel('Training Set Size') plt.show()
import numpy as np import linear_regression # Import the dataset X, Y = np.loadtxt("pizza.txt", skiprows=1, unpack=True) # Train the system with a learning rate of 0.00001 w, b = linear_regression.train(X, Y, iterations=10000, lr=0.00001) print("\nw=%.3f, b=%.3f" % (w, b)) # Predict the number of pizzas print("Prediction: x=%d => y=%.2f" % (20, linear_regression.predict(20, w, b)))
# !/usr/bin/python3 import my_dataset import neural_network import linear_regression import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' if False: data = my_dataset.random_linear() linear_regression.train(data) if False: train_dataset, test_normed_dataframe, test_labels_dataframe = my_dataset.auto_mpg( ) neural_network.mpg_train(train_dataset, test_normed_dataframe, test_labels_dataframe) if False: (x_train, y_train), (x_test, y_test), input_shape, num_classes = my_dataset.mnist() neural_network.mnist_train(x_train, y_train, x_test, y_test, input_shape, num_classes) if True: x_batch = my_dataset.synthetic_batch(4) neural_network.resnet50_train(x_batch)