Simply change col = pd.DataFrame(data, columns=[‘runs’,’balls’, ‘wickets’, ‘ground_average’, ‘pp_balls_left’, ‘total_overs’]) for col = pd.DataFrame([data], columns=[‘runs’,’balls’, ‘wickets’, ‘ground_average’, ‘pp_balls_left’, ‘total_overs’]) You want [data] for pandas to understand they’re rows. Simple illustration: a = [1, 2, 3] >>> pd.DataFrame(a) 0 0 1 1 2 2 3 >>> pd.DataFrame([a]) 0 1 2 0 1 2 3
The Y array in your screenshot is not a 1D array, it’s a 2D array with 300 rows and 1 column, as indicated by its shape being (300, 1). To remove the extra dimension, you can slice the array as Y[:, 0]. To generally convert an n-dimensional array to 1D, you can use np.reshape(a, a.size). … Read more
Setting timesteps = 1 (since, I want one timestep for each instance) and reshaping the X_train and X_test as: import numpy as np X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1])) X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1])) This worked!
This might happen inside scikit, and it depends on what you’re doing. I recommend reading the documentation for the functions you’re using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria. EDIT: How could I miss that: np.isnan(mat.any()) #and gets False np.isfinite(mat.all()) #and gets True … Read more
Just for the record: >>> int(‘55063.000000’) Traceback (most recent call last): File “<stdin>”, line 1, in <module> ValueError: invalid literal for int() with base 10: ‘55063.000000’ Got me here… >>> int(float(‘55063.000000’)) 55063 Has to be used!