dtellogaete

# Regresion logística

# Importar dataset
dataset = read.csv('Social_Network_Ads.csv')
dataset = dataset[, 3:5]

# Selección conjunto de entrenamiento y test
library(caTools)
set.seed(0)
split = sample.split(dataset$Purchased, SplitRatio = 0.75)

dtellogaete

# Importar librerías
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Importar el dataset de training
dataset = pd.read_csv('Social_Network_Ads.csv')
dataset['Age'] = dataset['Age'].fillna(dataset['Age'].mean())
dataset['EstimatedSalary'] = dataset['EstimatedSalary'].fillna(dataset['EstimatedSalary'].mean())
X = dataset.iloc[:len(dataset),[2,3]].values

dtellogaete

# Algoritmo de gradient descent
class LogisticRegressionGD(object):
    
    def __init__(self, l_rate = 0.1, n_iter =10000, random_state =1):
        self.l_rate = l_rate
        self.n_iter = n_iter
        self.random_state = random_state               
        
    def fit(self, X, y):
        rgen = np.random.RandomState(self.random_state)        

dtellogaete

# Importar el dataset
dataset = read.csv('Admission_Predict_Ver1.1.csv', sep = ",")
dataset = dataset[1:length(dataset$GRE.Score), c(2,9)]

# Selección conjunto de entrenamiento y test
library(caTools)
set.seed(0)
split = sample.split(dataset$GRE.Score, SplitRatio = 0.75)
training = subset(dataset, split == TRUE)
testing = subset(dataset, split == FALSE)

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# Función de Descenso de Gradiente

LinearRegressionGD = function(lrate = 0.1, niter = 10000,
                              X, y, theta){
  const = lrate*(1/length(X))
  for(i in 1:niter){
    h = X*theta[2]+theta[1]    
    theta[1] = theta[1]-const*(sum(h-y))
    theta[2] = theta[2]-const*(sum(h-y))*X
  }

dtellogaete

# Importar las librerías 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Importar el dataset de entranamiento
dataset = pd.read_csv('Admission_Predict_Ver1.1.csv')
X = dataset.iloc[:len(dataset), 1].values
X = X.reshape(-1,1)
X = np.insert(X, 0, 1, axis = 1)

dtellogaete

class LinearRegressionGD(object):
    
    def __init__(self, l_rate = 0.1, n_iter =10000):
        self.l_rate = l_rate
        self.n_iter = n_iter               
        
    def fit(self, X, y, theta):
        self.theta = theta
        X_value = X[:,1].reshape(-1, 1)
        const = self.l_rate*(1/X.shape[0])