issue: Estimating model w/ Batch Gradient Descent (BGD)
0
$begingroup$
I'm trying to estimate the model from the first code block w/Batch Gradient Descent (BGD) with an eta = 0.1, 1000 iterations, and 200 observations. The first block runs without error.
import numpy as np
np.random.seed(42)
#Generate random numbers between 0 and 1.
X = 2 + 2 * np.random.rand(200, 1)
Z = 3 - 3 * np.random.rand(200, 1)
Y = 5 + 2 * X + Z + np.random.randn(200, 1)
X_b = np.c_[np.ones((200, 1)), X, Z] # add x0 = 1 to each instance
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
theta_best
but then...
# set step at 0.1
eta = 0.1
# number of steps
n_iterations = 1000
# number of observations
m = 200
# randomly set the starting point
theta = np.random.randn(200,3)
# Walk 1000 steps.
for iteration in range(n_iterations):
gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
theta = theta - eta * gradients
theta
ValueError Traceback (most recent call last)
<ipython-input-12-dbb152611c75> in <module>()
8 # Walk 1000 steps.
9 for iteration in range(n_iterations):
---> 10 gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
11 theta = theta - eta * gradients
12 theta
ValueError: shapes (200,3) and (200,3) not aligned: 3 (dim 1) != 200 (dim 0)
What would explain the ValueError?
machine-learning python numpy jupyter
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
$begingroup$
I'm trying to estimate the model from the first code block w/Batch Gradient Descent (BGD) with an eta = 0.1, 1000 iterations, and 200 observations. The first block runs without error.
import numpy as np
np.random.seed(42)
#Generate random numbers between 0 and 1.
X = 2 + 2 * np.random.rand(200, 1)
Z = 3 - 3 * np.random.rand(200, 1)
Y = 5 + 2 * X + Z + np.random.randn(200, 1)
X_b = np.c_[np.ones((200, 1)), X, Z] # add x0 = 1 to each instance
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
theta_best
but then...
# set step at 0.1
eta = 0.1
# number of steps
n_iterations = 1000
# number of observations
m = 200
# randomly set the starting point
theta = np.random.randn(200,3)
# Walk 1000 steps.
for iteration in range(n_iterations):
gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
theta = theta - eta * gradients
theta
ValueError Traceback (most recent call last)
<ipython-input-12-dbb152611c75> in <module>()
8 # Walk 1000 steps.
9 for iteration in range(n_iterations):
---> 10 gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
11 theta = theta - eta * gradients
12 theta
ValueError: shapes (200,3) and (200,3) not aligned: 3 (dim 1) != 200 (dim 0)
What would explain the ValueError?
machine-learning python numpy jupyter
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
0
0
0
$begingroup$
I'm trying to estimate the model from the first code block w/Batch Gradient Descent (BGD) with an eta = 0.1, 1000 iterations, and 200 observations. The first block runs without error.
import numpy as np
np.random.seed(42)
#Generate random numbers between 0 and 1.
X = 2 + 2 * np.random.rand(200, 1)
Z = 3 - 3 * np.random.rand(200, 1)
Y = 5 + 2 * X + Z + np.random.randn(200, 1)
X_b = np.c_[np.ones((200, 1)), X, Z] # add x0 = 1 to each instance
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
theta_best
but then...
# set step at 0.1
eta = 0.1
# number of steps
n_iterations = 1000
# number of observations
m = 200
# randomly set the starting point
theta = np.random.randn(200,3)
# Walk 1000 steps.
for iteration in range(n_iterations):
gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
theta = theta - eta * gradients
theta
ValueError Traceback (most recent call last)
<ipython-input-12-dbb152611c75> in <module>()
8 # Walk 1000 steps.
9 for iteration in range(n_iterations):
---> 10 gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
11 theta = theta - eta * gradients
12 theta
ValueError: shapes (200,3) and (200,3) not aligned: 3 (dim 1) != 200 (dim 0)
What would explain the ValueError?
machine-learning python numpy jupyter
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to estimate the model from the first code block w/Batch Gradient Descent (BGD) with an eta = 0.1, 1000 iterations, and 200 observations. The first block runs without error.
import numpy as np
np.random.seed(42)
#Generate random numbers between 0 and 1.
X = 2 + 2 * np.random.rand(200, 1)
Z = 3 - 3 * np.random.rand(200, 1)
Y = 5 + 2 * X + Z + np.random.randn(200, 1)
X_b = np.c_[np.ones((200, 1)), X, Z] # add x0 = 1 to each instance
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
theta_best
but then...
# set step at 0.1
eta = 0.1
# number of steps
n_iterations = 1000
# number of observations
m = 200
# randomly set the starting point
theta = np.random.randn(200,3)
# Walk 1000 steps.
for iteration in range(n_iterations):
gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
theta = theta - eta * gradients
theta
ValueError Traceback (most recent call last)
<ipython-input-12-dbb152611c75> in <module>()
8 # Walk 1000 steps.
9 for iteration in range(n_iterations):
---> 10 gradients = 2/m * X_b.T.dot(X_b.dot(theta) - Y)
11 theta = theta - eta * gradients
12 theta
ValueError: shapes (200,3) and (200,3) not aligned: 3 (dim 1) != 200 (dim 0)
What would explain the ValueError?
machine-learning python numpy jupyter
machine-learning python numpy jupyter
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
asked 23 hours ago
Kyle AnthonyKyle Anthony
1
1
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kyle Anthony is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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