def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [convert_variables([-1, -1]), convert_variables([1, 1])]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([-math.pi, 12.275]), convert_variables([math.pi, 2.275]), convert_variables([9.42478, 2.475]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [convert_variables([-0.029896, 0])]
def get_approximate_options(n, m, methods, losses): #X, y = load_boston(return_X_y=True) #x = X[:100] #x_validate = X[420:] x = [[y] * m for y in range(1, 25)] x_validate = [[y + 0.5] * m for y in range(0, 25)] #x = [[y] * m for y in range(1, 101)] #x_validate = [[y + 0.5] * m for y in range(0, 100)] #X, y = load_diabetes(return_X_y=True) #x = X[:50] #x_validate = X[400:] options = [] for method in methods: options.append({ "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [ convert_variables_without_trainable(x_tmp) for x_tmp in x_validate ], "params": convert_variables([1 for x in range(n)]), "loss_function": losses, "opt": method, "eps": 0.0001, "max_steps": 100, }) return options
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([-pi, 12.275]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([math.pi, math.pi]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([0.78547, 0.78547, 0.78547]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([0, 1.253115]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([420.9687, 420.9687]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([-0.547198, -1.5472]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([512, 404.2319]), ]
def get_minimum(): """Get list of function minimimums :return: list of function minimimums :rtype: list """ return [ convert_variables([ 3.0814879110195774e-31, ]), ]
""" Approximate options """ approximate_options2 = [ { "x": [ convert_variables_without_trainable([x, x]) for x in range(-10, 10, 1) ], "x_validate": [ convert_variables_without_trainable([x, x]) for x in range(-11, 10, 1) ], "params": convert_variables([1 for x in range(11)]), "loss_function": tf.keras.losses.MAE, "opt": tf.keras.optimizers.Adam(learning_rate=0.001), "eps": 0.0001, "max_steps": 1000, }, { "x": [ convert_variables_without_trainable([x, x]) for x in range(-10, 10, 1) ], "x_validate": [
from utils.utils import convert_variables, convert_variables_without_trainable """ Approximate options """ X, y = load_boston(return_X_y=True) x = X[:100] x_validate = X[420:] approximate_options5 = [ { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(82)]), "loss_function": tf.keras.losses.MAE, "opt": tf.keras.optimizers.SGD(learning_rate=1, momentum=0.1), "eps": 0.0001, "max_steps": 30, }, { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(82)]),
import numpy as np import tensorflow as tf from utils.utils import convert_variables, convert_variables_without_trainable """ Approximate options """ approximate_options1 = [ { "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))], "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))], "params": convert_variables([1 for x in range(11)]), "loss_function": tf.keras.losses.MAE, "opt": tf.keras.optimizers.Adam(learning_rate=0.001), "eps": 0.0001, "max_steps": 1000, }, { "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))], "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))], "params": convert_variables([1 for x in range(11)]), "loss_function": tf.keras.losses.MAE, "opt": tf.keras.optimizers.Adam(learning_rate=0.01), "eps": 0.0001, "max_steps": 10000, }, { "x": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0, 5, 0.2))], "x_validate": [convert_variables_without_trainable([x, x]) for x in list(np.arange(0.1, 5, 0.02))],
import tensorflow as tf from utils.utils import convert_variables """ Minimize options """ schmitt_wetters_function_options = [ { "x": convert_variables([-1, 1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.0002), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([-1, 1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.0004), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([-1, 1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.0006), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([-1, 1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.0008), "eps": 0.0001, "max_steps": 100,
from utils.generate_data import generate_set from sklearn.datasets import load_boston X, y = load_boston(return_X_y=True) x = X[:100] x_validate = X[420:] approximate_options9_2 = [ { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(261)]), "loss_function": tf.keras.losses.MSE, "opt": tf.keras.optimizers.SGD(learning_rate=1), "eps": 0.0001, "max_steps": 30, }, { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(261)]),
import tensorflow as tf from utils.utils import convert_variables """ Minimize options """ zettla_function_options = [ { "x": convert_variables([1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.05), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.01), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.5), "eps": 0.0001, "max_steps": 100, }, { "x": convert_variables([1, 1]), "opt": tf.keras.optimizers.SGD(learning_rate=0.1), "eps": 0.0001, "max_steps": 100,
import tensorflow as tf from utils.utils import convert_variables """ Minimize options """ smooth_function_options3 = [ { "x": convert_variables([10, 10]), "opt": tf.keras.optimizers.Adagrad(learning_rate=18.45), "eps": 0.0001, "max_steps": 1000, }, { "x": convert_variables([10, 10]), "opt": tf.keras.optimizers.Adagrad(learning_rate=18.47), "eps": 0.0001, "max_steps": 1000, }, { "x": convert_variables([10, 10]), "opt": tf.keras.optimizers.Adagrad(learning_rate=18.49), "eps": 0.0001, "max_steps": 1000, }, { "x": convert_variables([10, 10]), "opt": tf.keras.optimizers.Adagrad(learning_rate=18.51), "eps": 0.0001, "max_steps": 1000,
from utils.utils import convert_variables, convert_variables_without_trainable from utils.generate_data import generate_set from sklearn.datasets import load_boston X, y = load_boston(return_X_y=True) x = X[:100] x_validate = X[420:] approximate_options9_3 = [ { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(79)]), "loss_function": tf.keras.losses.MSE, "opt": tf.keras.optimizers.SGD(learning_rate=1), "eps": 0.0001, "max_steps": 30, }, { "x": [convert_variables_without_trainable(x_tmp) for x_tmp in x], "x_validate": [convert_variables_without_trainable(x_tmp) for x_tmp in x_validate], "params": convert_variables([5 for x in range(79)]), "loss_function": tf.keras.losses.MSE, "opt": tf.keras.optimizers.SGD(learning_rate=0.1), "eps": 0.0001, "max_steps": 30, }, {
import tensorflow as tf from utils.utils import convert_variables """ Minimize options """ kin_function_options = [ { "x": convert_variables([0, 1.4]), "opt": tf.keras.optimizers.Adagrad(learning_rate=10), "eps": 0.001, "max_steps": 100, }, { "x": convert_variables([0, 1.4]), "opt": tf.keras.optimizers.Adam(learning_rate=20), "eps": 0.001, "max_steps": 100, }, { "x": convert_variables([0, 1.4]), "opt": tf.keras.optimizers.Adamax(learning_rate=2), "eps": 0.001, "max_steps": 100, }, ]