Optimization Error for Logistic Regression using Scipy.opt
I've been trying to write Andrew NG's Logistic Regression Problem Using python and Scipy.opt for optimizing the function. However, I get a VALUE ERROR that says I have mismatching dimensions. I've tried to flatten() my theta array as scipy.opt doesn't seem to work very well with single column/row vector, however the problem still persists. I've also reshaped the array, but the code doesn't respond to it and shows the same error.
Kindly point me in the right direction as to what is causing the problem and how to avoid it.
Thanks a million!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())
positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]
#Visualizing Dataset
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.xlabel("Exam 1 Score")
plt.ylabel("Exam 2 Score")
plt.title("Admission Graph")
plt.legend()
#plt.show()
#Preprocessing Data
dataset.insert(0, "x0", 1)
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values
y = dataset.iloc[:,col-1:col].values
b = np.zeros([1,col-1])
m = len(y)
print(f"X Shape: {x.shape} Y Shape: {y.shape} B Shape: {b.shape}")
#Defining Functions
def hypothesis(x, y, b):
h = 1 / (1+np.exp(-x @ b.T))
return h
def cost(x, y, b):
first = (y.T @ np.log(hypothesis(x, y, b)))
second = (1-y).T @ np.log(1 - hypothesis(x, y, b))
j = (-1/m) * np.sum(first+second)
return j
def gradient(x, y, b):
grad_step = ((hypothesis(x, y, b) - y) @ x.T) / m
return b
#Output
initial_cost = cost(x, y, b)
print(f"nInitial Cost = {initial_cost}")
final_cost = opt.fmin_tnc(func=cost, x0=b.flatten() , fprime=gradient, args=(x,y))
print(f"Final Cost = {final_cost} nTheta = {b}")
Dataset : Student Dataset.txt
machine-learning python logistic-regression scipy
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
migrated from datascience.stackexchange.com Nov 20 '18 at 22:31
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
I've been trying to write Andrew NG's Logistic Regression Problem Using python and Scipy.opt for optimizing the function. However, I get a VALUE ERROR that says I have mismatching dimensions. I've tried to flatten() my theta array as scipy.opt doesn't seem to work very well with single column/row vector, however the problem still persists. I've also reshaped the array, but the code doesn't respond to it and shows the same error.
Kindly point me in the right direction as to what is causing the problem and how to avoid it.
Thanks a million!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())
positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]
#Visualizing Dataset
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.xlabel("Exam 1 Score")
plt.ylabel("Exam 2 Score")
plt.title("Admission Graph")
plt.legend()
#plt.show()
#Preprocessing Data
dataset.insert(0, "x0", 1)
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values
y = dataset.iloc[:,col-1:col].values
b = np.zeros([1,col-1])
m = len(y)
print(f"X Shape: {x.shape} Y Shape: {y.shape} B Shape: {b.shape}")
#Defining Functions
def hypothesis(x, y, b):
h = 1 / (1+np.exp(-x @ b.T))
return h
def cost(x, y, b):
first = (y.T @ np.log(hypothesis(x, y, b)))
second = (1-y).T @ np.log(1 - hypothesis(x, y, b))
j = (-1/m) * np.sum(first+second)
return j
def gradient(x, y, b):
grad_step = ((hypothesis(x, y, b) - y) @ x.T) / m
return b
#Output
initial_cost = cost(x, y, b)
print(f"nInitial Cost = {initial_cost}")
final_cost = opt.fmin_tnc(func=cost, x0=b.flatten() , fprime=gradient, args=(x,y))
print(f"Final Cost = {final_cost} nTheta = {b}")
Dataset : Student Dataset.txt
machine-learning python logistic-regression scipy
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
migrated from datascience.stackexchange.com Nov 20 '18 at 22:31
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
I've been trying to write Andrew NG's Logistic Regression Problem Using python and Scipy.opt for optimizing the function. However, I get a VALUE ERROR that says I have mismatching dimensions. I've tried to flatten() my theta array as scipy.opt doesn't seem to work very well with single column/row vector, however the problem still persists. I've also reshaped the array, but the code doesn't respond to it and shows the same error.
Kindly point me in the right direction as to what is causing the problem and how to avoid it.
Thanks a million!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())
positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]
#Visualizing Dataset
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.xlabel("Exam 1 Score")
plt.ylabel("Exam 2 Score")
plt.title("Admission Graph")
plt.legend()
#plt.show()
#Preprocessing Data
dataset.insert(0, "x0", 1)
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values
y = dataset.iloc[:,col-1:col].values
b = np.zeros([1,col-1])
m = len(y)
print(f"X Shape: {x.shape} Y Shape: {y.shape} B Shape: {b.shape}")
#Defining Functions
def hypothesis(x, y, b):
h = 1 / (1+np.exp(-x @ b.T))
return h
def cost(x, y, b):
first = (y.T @ np.log(hypothesis(x, y, b)))
second = (1-y).T @ np.log(1 - hypothesis(x, y, b))
j = (-1/m) * np.sum(first+second)
return j
def gradient(x, y, b):
grad_step = ((hypothesis(x, y, b) - y) @ x.T) / m
return b
#Output
initial_cost = cost(x, y, b)
print(f"nInitial Cost = {initial_cost}")
final_cost = opt.fmin_tnc(func=cost, x0=b.flatten() , fprime=gradient, args=(x,y))
print(f"Final Cost = {final_cost} nTheta = {b}")
Dataset : Student Dataset.txt
machine-learning python logistic-regression scipy
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
I've been trying to write Andrew NG's Logistic Regression Problem Using python and Scipy.opt for optimizing the function. However, I get a VALUE ERROR that says I have mismatching dimensions. I've tried to flatten() my theta array as scipy.opt doesn't seem to work very well with single column/row vector, however the problem still persists. I've also reshaped the array, but the code doesn't respond to it and shows the same error.
Kindly point me in the right direction as to what is causing the problem and how to avoid it.
Thanks a million!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as opt
dataset = pd.read_csv("Students Exam Dataset.txt", names=["Exam 1", "Exam 2", "Admitted"])
print(dataset.head())
positive = dataset[dataset["Admitted"] == 1]
negative = dataset[dataset["Admitted"] == 0]
#Visualizing Dataset
plt.scatter(positive["Exam 1"], positive["Exam 2"], color="blue", marker="o", label="Admitted")
plt.scatter(negative["Exam 1"], negative["Exam 2"], color="red", marker="x", label="Not Admitted")
plt.xlabel("Exam 1 Score")
plt.ylabel("Exam 2 Score")
plt.title("Admission Graph")
plt.legend()
#plt.show()
#Preprocessing Data
dataset.insert(0, "x0", 1)
col = len(dataset.columns)
x = dataset.iloc[:,0:col-1].values
y = dataset.iloc[:,col-1:col].values
b = np.zeros([1,col-1])
m = len(y)
print(f"X Shape: {x.shape} Y Shape: {y.shape} B Shape: {b.shape}")
#Defining Functions
def hypothesis(x, y, b):
h = 1 / (1+np.exp(-x @ b.T))
return h
def cost(x, y, b):
first = (y.T @ np.log(hypothesis(x, y, b)))
second = (1-y).T @ np.log(1 - hypothesis(x, y, b))
j = (-1/m) * np.sum(first+second)
return j
def gradient(x, y, b):
grad_step = ((hypothesis(x, y, b) - y) @ x.T) / m
return b
#Output
initial_cost = cost(x, y, b)
print(f"nInitial Cost = {initial_cost}")
final_cost = opt.fmin_tnc(func=cost, x0=b.flatten() , fprime=gradient, args=(x,y))
print(f"Final Cost = {final_cost} nTheta = {b}")
Dataset : Student Dataset.txt
machine-learning python logistic-regression scipy
machine-learning python logistic-regression scipy
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
asked Nov 20 '18 at 2:42
Antony JohnAntony John
337
337
migrated from datascience.stackexchange.com Nov 20 '18 at 22:31
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
migrated from datascience.stackexchange.com Nov 20 '18 at 22:31
This question came from our site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field.
add a comment |
add a comment |
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