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My function (name CovexHull(point)) accepts argument as 2 dimensional array.
hull = ConvexHull(points)
In [1]: points.ndim Out[1]: 2 In [2]: points.shape Out[2]: (10, 2) In [3]: points Out[3]: array([[ 0. , 0. ], [ 1. , 0.8], [ 0.9, 0.8], [ 0.9, 0.7], [ 0.9, 0.6], [ 0.8, 0.5], [ 0.8, 0.5], [ 0.7, 0.5], [ 0.1, 0. ], [ 0. , 0. ]]) points is a numpy array with ndim 2.
I have 2 different numpy arrays (tp and fp) like below
In [4]: fp.ndim Out[4]: 1 In [5]: fp.shape Out[5]: (10,) In [6]: fp Out[6]: array([ 0. , 0.1, 0.2, 0.3, 0.4, 0.4, 0.5, 0.6, 0.9, 1. ]) I want to know how can I create a 2 dimensional numpy array effectively (like points mentioned above) with tp and fp.
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Use reshape() Function to Transform 1d Array to 2d Array The number of components within every dimension defines the form of the array. We may add or delete parameters or adjust the number of items within every dimension by using reshaping. To modify the layout of a NumPy ndarray, we will be using the reshape() method.
You can even create a two-dimensional array where each subarray has a different length or different type, also known as a heterogeneous array in Java.
Call the function input_array to store elements in 1D array. Call the function print_array to print the elements of 1D array. Call the function array_to_matrix to convert 1D array to 2D array. Call function print_matrix to print the elements of the 2D array.
If you wish to combine two 10 element 1-d arrays into a 2-d array np.vstack((tp, fp)).T will do it. np.vstack((tp, fp)) will return an array of shape (2, 10), and the T attribute returns the transposed array with shape (10, 2) (i.e. with the two 1-d arrays forming columns rather than rows).
>>> tp = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> tp.ndim 1 >>> tp.shape (10,) >>> fp = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19]) >>> fp.ndim 1 >>> fp.shape (10,) >>> combined = np.vstack((tp, fp)).T >>> combined array([[ 0, 10], [ 1, 11], [ 2, 12], [ 3, 13], [ 4, 14], [ 5, 15], [ 6, 16], [ 7, 17], [ 8, 18], [ 9, 19]]) >>> combined.ndim 2 >>> combined.shape (10, 2) You can use numpy's column_stack
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