import numpy
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import time
from random import randint
import analise_auxiliar
array_passe: numpy.ndarray = numpy.concatenate([
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2021-VIRTUAL/DIVISION-B/ER_FORCE/ATA/*Pass.csv"),
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2021-VIRTUAL/DIVISION-B/KIKS/ATA/*Pass.csv"),
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2021-VIRTUAL/DIVISION-B/RoboCin/ATA/*Pass.csv"),
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2021-VIRTUAL/DIVISION-B/RoboFEI/ATA/*Pass.csv"),
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2021-VIRTUAL/DIVISION-B/TIGERs_Mannheim/ATA/*Pass.csv")
])
X, y = analise_auxiliar.get_x_y_passes(array_passe, 1.12)
x_axis: numpy.ndarray = numpy.fromiter(range(0, 500, 1), dtype=numpy.uint16)
score_train: numpy.ndarray = numpy.full(x_axis.shape, 0, dtype=numpy.float64)
score_test: numpy.ndarray = numpy.full(x_axis.shape, 0, dtype=numpy.float64)
cofs = None
start: float = time.time()
for j, i in enumerate(x_axis):
import numpy
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from matplotlib import pyplot
import joblib
import time
from random import randint
import analise_auxiliar
pyplot.style.use('dark_background')
array_chute: numpy.ndarray = numpy.concatenate([
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2019/ER_FORCE/ATA/*Chute.csv"),
analise_auxiliar.get_array_from_pattern(
"ROBOCUP-2019/ZJUNlict/ATA/*Chute.csv")
])
y: numpy.ndarray = array_chute[:, 0]
X: numpy.ndarray = array_chute[:, [1, 2, 3]]
model_out: DecisionTreeRegressor = DecisionTreeRegressor(
criterion='mse',
splitter='best',
max_depth=3,
min_samples_split=100 * 1e-3,
min_samples_leaf=100 * 1e-3,
min_weight_fraction_leaf=100 * 1e-3,
max_features='auto',
random_state=randint(0, 1000),
max_leaf_nodes=5,
if numpy.size(var) == 0:
continue
for j, _var in enumerate(
distances_weights[i]
): # apply the weight function for each distance
if (_var >= MAXIMUM_DISTANCE):
distances_weights[i][j] = 0.0001
continue
distances_weights[i][j] = 1 - _var / MAXIMUM_DISTANCE
return distances_weights
array_passe: numpy.ndarray = analise_auxiliar.get_array_from_pattern(
"ALL/*Passe.csv")
y: numpy.ndarray = array_passe[:, 0]
X: numpy.ndarray = array_passe[:, [1, 2, 3, 4, 4, 6, 7, 8]]
knn_out: KNeighborsRegressor = KNeighborsRegressor(
n_neighbors=20, weights=customized_weights_linear, n_jobs=1).fit(X, y)
joblib.dump(knn_out, "models/avaliacao_passe_knn_with_weights.sav")
x_axis: numpy.ndarray = numpy.fromiter(range(1, 50), dtype=numpy.uint16)
score_train: numpy.ndarray = numpy.full(x_axis.shape, 0, dtype=numpy.float64)
score_test: numpy.ndarray = numpy.full(x_axis.shape, 0, dtype=numpy.float64)
start: float = time.time()
import numpy
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
import joblib
import time
from random import randint
import analise_auxiliar
array_passe: numpy.ndarray = analise_auxiliar.get_array_from_pattern(
"LARC-2020-VIRTUAL/ALL/*Passe.csv")
X, y = analise_auxiliar.get_x_y_passes(array_passe, 1.02)
tree_out: DecisionTreeRegressor = DecisionTreeRegressor(
criterion='mse',
splitter='best',
max_depth=4,
min_samples_split=33 * 1e-3,
min_samples_leaf=80 * 1e-3,
min_weight_fraction_leaf=87e-3,
max_features='auto',
random_state=38,
max_leaf_nodes=6,
min_impurity_decrease=0,
min_impurity_split=None,
presort='deprecated',
ccp_alpha=40).fit(X, y)
joblib.dump(tree_out, "models/avaliacao_passe_tree.sav")
x_axis: numpy.ndarray = numpy.fromiter(range(1, 1000, 10), dtype=numpy.uint16)
