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Infinite loop was fixed. #1105

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merged 4 commits into from
Aug 7, 2019
Merged

Infinite loop was fixed. #1105

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67ddf3a
Infinite loop was fixed.
AmritK10 Aug 6, 2019
94e585c
Update logistic_regression.py
poyea Aug 7, 2019
2093ea6
Update logistic_regression.py
poyea Aug 7, 2019
a9acf07
correct spacing according to PEP8
AmritK10 Aug 7, 2019
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27 changes: 7 additions & 20 deletions machine_learning/logistic_regression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,34 +40,20 @@ def logistic_reg(
alpha,
X,
y,
num_steps,
max_iterations=70000,
):
converged = False
iterations = 0
theta = np.zeros(X.shape[1])

while not converged:
for iterations in range(max_iterations):
z = np.dot(X, theta)
h = sigmoid_function(z)
gradient = np.dot(X.T, h - y) / y.size
theta = theta - alpha * gradient
theta = theta - alpha * gradient # updating the weights
z = np.dot(X, theta)
h = sigmoid_function(z)
J = cost_function(h, y)
iterations += 1 # update iterations
weights = np.zeros(X.shape[1])
for step in range(num_steps):
scores = np.dot(X, weights)
predictions = sigmoid_function(scores)
if step % 10000 == 0:
print(log_likelihood(X,y,weights)) # Print log-likelihood every so often
return weights

if iterations == max_iterations:
print('Maximum iterations exceeded!')
print('Minimal cost function J=', J)
converged = True
if iterations % 100 == 0:
print(f'loss: {J} \t') # printing the loss after every 100 iterations
return theta

# In[68]:
Expand All @@ -78,8 +64,8 @@ def logistic_reg(
y = (iris.target != 0) * 1

alpha = 0.1
theta = logistic_reg(alpha,X,y,max_iterations=70000,num_steps=30000)
print(theta)
theta = logistic_reg(alpha,X,y,max_iterations=70000)
print("theta: ",theta) # printing the theta i.e our weights vector


def predict_prob(X):
Expand All @@ -105,3 +91,4 @@ def predict_prob(X):
)

plt.legend()
plt.show()