Mixing
ValueError: shapes (993,228) and (1,228) not aligned: 228 (dim 1) != 1 (dim 0)
I have created a OLS linear regression model in python and when I predict for a particular value I get the error.
My code is below:
df=pd.read_csv("smatrix.csv",index_col=0)
import statsmodels.api as sm
x=df.iloc[:,:-1]
y=df.Rating
est = sm.OLS(y.astype(float), x.astype(float))
results=est.fit()
op=list()
for i in df.columns:
if 'bad' == i:
op.append(1)
else:
op.append(0)
op=op[:-1]
X5=np.array(op).reshape(1,-1)
y1=est.predict(X5)
The error that I am getting is
ValueError: shapes (993,228) and (1,228) not aligned: 228 (dim 1) != 1 (dim 0)
The shape of X5 is (1, 228)
The shape of x is (993, 228)
The shape of y is (993,)
python-3.x numpy valueerror
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
add a comment |
I have created a OLS linear regression model in python and when I predict for a particular value I get the error.
My code is below:
df=pd.read_csv("smatrix.csv",index_col=0)
import statsmodels.api as sm
x=df.iloc[:,:-1]
y=df.Rating
est = sm.OLS(y.astype(float), x.astype(float))
results=est.fit()
op=list()
for i in df.columns:
if 'bad' == i:
op.append(1)
else:
op.append(0)
op=op[:-1]
X5=np.array(op).reshape(1,-1)
y1=est.predict(X5)
The error that I am getting is
ValueError: shapes (993,228) and (1,228) not aligned: 228 (dim 1) != 1 (dim 0)
The shape of X5 is (1, 228)
The shape of x is (993, 228)
The shape of y is (993,)
python-3.x numpy valueerror
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
add a comment |
I have created a OLS linear regression model in python and when I predict for a particular value I get the error.
My code is below:
df=pd.read_csv("smatrix.csv",index_col=0)
import statsmodels.api as sm
x=df.iloc[:,:-1]
y=df.Rating
est = sm.OLS(y.astype(float), x.astype(float))
results=est.fit()
op=list()
for i in df.columns:
if 'bad' == i:
op.append(1)
else:
op.append(0)
op=op[:-1]
X5=np.array(op).reshape(1,-1)
y1=est.predict(X5)
The error that I am getting is
ValueError: shapes (993,228) and (1,228) not aligned: 228 (dim 1) != 1 (dim 0)
The shape of X5 is (1, 228)
The shape of x is (993, 228)
The shape of y is (993,)
python-3.x numpy valueerror
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
I have created a OLS linear regression model in python and when I predict for a particular value I get the error.
My code is below:
df=pd.read_csv("smatrix.csv",index_col=0)
import statsmodels.api as sm
x=df.iloc[:,:-1]
y=df.Rating
est = sm.OLS(y.astype(float), x.astype(float))
results=est.fit()
op=list()
for i in df.columns:
if 'bad' == i:
op.append(1)
else:
op.append(0)
op=op[:-1]
X5=np.array(op).reshape(1,-1)
y1=est.predict(X5)
The error that I am getting is
ValueError: shapes (993,228) and (1,228) not aligned: 228 (dim 1) != 1 (dim 0)
The shape of X5 is (1, 228)
The shape of x is (993, 228)
The shape of y is (993,)
python-3.x numpy valueerror
python-3.x numpy valueerror
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
asked Jan 2 at 8:12
Hemant SambhaHemant Sambha
84
84
add a comment |
add a comment |
1 Answer
1
active
oldest
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est.predict() expects the first argument to be params (more here), but you are passing X[5] of shape 1, 128. The error message is thrown when the model tries to multiply X with params(in this case X[5]).
X -> (993, 128)
params -> (1, 128)
This two matrices(X, params) cannot be multiplied because the columns of X(128) is not aligned with rows of params(1).
Solution
Use the parameters learned with fit method.
y1=est.predict(results.params, X5)
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
add a comment |
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1 Answer
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active
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
est.predict() expects the first argument to be params (more here), but you are passing X[5] of shape 1, 128. The error message is thrown when the model tries to multiply X with params(in this case X[5]).
X -> (993, 128)
params -> (1, 128)
This two matrices(X, params) cannot be multiplied because the columns of X(128) is not aligned with rows of params(1).
Solution
Use the parameters learned with fit method.
y1=est.predict(results.params, X5)
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
add a comment |
est.predict() expects the first argument to be params (more here), but you are passing X[5] of shape 1, 128. The error message is thrown when the model tries to multiply X with params(in this case X[5]).
X -> (993, 128)
params -> (1, 128)
This two matrices(X, params) cannot be multiplied because the columns of X(128) is not aligned with rows of params(1).
Solution
Use the parameters learned with fit method.
y1=est.predict(results.params, X5)
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
add a comment |
est.predict() expects the first argument to be params (more here), but you are passing X[5] of shape 1, 128. The error message is thrown when the model tries to multiply X with params(in this case X[5]).
X -> (993, 128)
params -> (1, 128)
This two matrices(X, params) cannot be multiplied because the columns of X(128) is not aligned with rows of params(1).
Solution
Use the parameters learned with fit method.
y1=est.predict(results.params, X5)
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
est.predict() expects the first argument to be params (more here), but you are passing X[5] of shape 1, 128. The error message is thrown when the model tries to multiply X with params(in this case X[5]).
X -> (993, 128)
params -> (1, 128)
This two matrices(X, params) cannot be multiplied because the columns of X(128) is not aligned with rows of params(1).
Solution
Use the parameters learned with fit method.
y1=est.predict(results.params, X5)
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
answered Jan 2 at 8:29
MitikuMitiku
2,3391418
2,3391418
add a comment |
add a comment |
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