Abraham Zamudio Chauca robintux

robintux / Errores

Created September 14, 2024 02:14

	Traceback (most recent call last):

	Cell In[96], line 25
	grid_search_result = grid_search.fit(Xtrain, Ytrain)

	File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/base.py:1473 in wrapper
	return fit_method(estimator, args, *kwargs)

	File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1018 in fit
	self._run_search(evaluate_candidates)

robintux / scikeras_ValueError.error

Last active September 13, 2024 22:00

	Traceback (most recent call last):

	File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/spyder_kernels/customize/utils.py:209 in exec_encapsulate_locals
	exec_fun(compile(code_ast, filename, "exec"), globals)

	File /mnt/251793b5-818c-4841-b9d4-2c47bb98fef0/CTIC/PDE_MachineLearning_CTIC/Clases_Modulos_6_7_8/Clases2024/sesion2/script01.py:151
	grid_search_result = grid_search.fit(Xtrain, Ytrain)

	File ~/anaconda3/envs/python310_keras215_numpy126/lib/python3.10/site-packages/sklearn/base.py:1473 in wrapper
	return fit_method(estimator, args, *kwargs)

robintux / scikeras_ValueError.py

Last active September 13, 2024 22:00


	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	from sklearn.model_selection import train_test_split

	# Keras
	import keras

robintux / Problem_with_Graph_execution_error.py

Created September 11, 2024 02:00

	# %% Primer Paso : Datos y modulos

	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns

	# Keras
	from keras.layers import Dense
	from keras.models import Sequential

robintux / hello_world.cu

Last active September 8, 2024 03:04

	#include <stdio.h>

	__global__ void helloCUDA()
	{
	printf("Hello, CUDA!\n Abraham Zamudio");
	}

	int main()
	{
	helloCUDA<<<1, 1>>>();

robintux / my_smote_2024.py

Last active September 4, 2024 12:48

	import warnings
	import random

	import numpy as np
	from sklearn.neighbors import NearestNeighbors

	def smote(T, N, K):
	"""
	T ~ an array-like object representing the minority matrix
	N ~ the percent oversampling you want. e.g. 500 will give you 5 samples

robintux / AreaCirculo5.py

Created May 30, 2024 00:17

	import math as m
	import random as rnd
	import os
	r = print("Ingresa el radio de la circunferencia : ")
	AreaCirculo = m.pi*complex(r)+2
	print("El area del circulo es : ", AreaCirculo)

robintux / AreaCirculo4.py

Created May 30, 2024 00:16

	import math as m
	import random as rnd
	import os
	r = input("Ingresa el radio de la circunferencia : ")
	AreaCirculo = m.osfloat(r)*2
	print("El area del circulo es : ", r+2)

robintux / AreaCirculo3.py

Created May 30, 2024 00:16

	import math as m
	import random as rnd
	import os
	r = input("Ingresa el radio de la circunferencia : ")
	AreaCirculo = rnd.pi*float(r)+2
	print("El area del circulo es : ", AreaCirculo)

robintux / AreaCirculo2.py

Created May 30, 2024 00:15

	import math as m
	import random as rnd
	import os
	r = input("Ingresa el radio de la circunferencia : ")
	AreaCirculo = m.pi*float(r)+2
	print("El area del circulo es : ", AreaCirculo)