# Predict function MLS Course 1 Week3

I need help on the prediction function code in the if clause.

def predict(X, w, b):
“”"
Predict whether the label is 0 or 1 using learned logistic
regression parameters w

``````Args:
X : (ndarray Shape (m, n))
w : (array_like Shape (n,))      Parameters of the model
b : (scalar, float)              Parameter of the model

Returns:
p: (ndarray (m,1))
The predictions for X using a threshold at 0.5
"""
# number of training examples
m, n = X.shape
p = np.zeros(m)

### START CODE HERE ###
# Loop over each example
for i in range(m):

z_wb = 0
# Loop over each feature
for j in range(n):
z_wb_ij = X[i, j] * w[j]
z_wb += z_wb_ij

z_wb += b
f_wb = sigmoid(z_wb)
p = f_wb

if p >= 0.5:
p = 1
else:
p = 0

return p
``````

np.random.seed(1)
tmp_w = np.random.randn(2)
tmp_b = 0.3
tmp_X = np.random.randn(4, 2) - 0.5

tmp_p = predict(tmp_X, tmp_w, tmp_b)
print(f’Output of predict: shape {tmp_p.shape}, value {tmp_p}')

# UNIT TESTS

predict_test(predict)

AttributeError Traceback (most recent call last)
in
6
7 tmp_p = predict(tmp_X, tmp_w, tmp_b)
----> 8 print(f’Output of predict: shape {tmp_p.shape}, value {tmp_p}')
9
10 # UNIT TESTS

AttributeError: ‘int’ object has no attribute ‘shape’

As you see, `p` is initialized as `p = np.zeros(m)`. It is a vector with size=`m`.
But, in the for-loop, you set “integer 1 or 0” to` p` directly. This redefined “`p`” as "int. And, of course, “int” has no attribute ‘shape’.
Think about why you are using for-loop with iteration count = `m`. You need to set calculated value into each element in `p` vector like `p[i]`.

By the way, posting your code is not recommended. Please remove it.