![]() Min_impurity_decrease=0. > DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=1, ![]() Y_train = np.reshape(np.array(Y_train), (dimY2, ))ĭtree= DecisionTreeRegressor(random_state=0, max_depth=1) Y_train = np.array()]) For example, a common voxel size is 3 by 3 by 4 millimeters. A typical fMRI image is composed of voxels, which are like the pixels that make up your computer screen, but in three dimensions. X_train = np.reshape(np.array(X_train), (dimX1*dimX2, dimX3)) Answer: Resampling means changing either the resolution, dimensions, or both the resolution and dimensions of an image. This matrix AS, however, is labeled at the top by dim1, dim2. X_train = np.array(,ĭimX1, dimX2, dimX3 = np.array(X_train).shape I have a matrix called AS (in R) that collects indices of elements of an array that satisfy a certain condition. fslinfo datatype FLOAT32 dim1 64 dim2 64 dim3 33 dim4 1 datatype 16 pixdim1 3.125000 pixdim2 3.125000 pixdim3 4.000000 pixdim4 2.000000 calmax 1.0000 calmin 0. You need to separate them and passing as (30, ), as follows: from ee import DecisionTreeRegressor Furthermore, your Y_train has shape (1, 30) which means you are passing 30 data at once. Your are passing a 3D array with X_train, and fit only allows you to be 2D. The reason why you can't train your data is because 2 things: One solution can be:ĪMSWER EDITTED AFTER READING HIS/HER COMMENTS Yarray-like of shape (n_samples,) or (n_samples, n_outputs)įit function expects 2D arrays in both X and Y arrays. any suggestions?įirst of all, I recommend you to convert X and Y as numpy arrays, but I can not be 100% sure if your variables are indeed, since you haven't uploaded your code here. and couldn't solve it with reshape since these are lists. fslinfo datatype FLOAT32 dim1 64 dim2 64 dim3 33 dim4 1 datatype 16 pixdim1 3.125000 pixdim2 3.125000 pixdim3 4.000000 pixdim4 2.000000 calmax 1.0000 calmin 0.0000 filetype NIFTI-1+ Reply Quote. I get the error ValueError: Found array with dim 3. ![]() When i try to perform dtree= DecisionTreeRegressor(random_state=0, max_depth=1) My train data x is this: ,Īnd y: )] At sub-brick #0 '?' datum type is float: 0 to 1127įslinfo bold_pvalues_contrast1_ am trying to perform decision trees with some train and test data which are in lists named x&y. Number of time steps = 160 Time step = 2.00000s Origin = 0.00000s Storage Space: 540,672 (541 thousand ) bytes Identifier Code: NII_wFvfILlbwjznQCEILpaJQ Creation Date: Wed Apr 29 14:38:11 2015īyte Order: LSB_FIRST ** WARNING: NIfTI file bold_pvalues_contrast1_ dimensions altered since AFNI extension was addedĭataset File: bold_pvalues_contrast1_ OutputNiftiStatisticsEPI->nvox = EPI_DATA_W * EPI_DATA_H * EPI_DATA_D Nifti_image *outputNiftiStatisticsEPI = nifti_copy_nim_info(inputfMRI) How do I change the nifti header such that AFNI understands that there is only one volume? If I look at it with fslinfo it says that there is only one volume. But fslinfo > says me every image has same 2mm isotropic voxel size. If I look at it with 3dinfo it says that there are 160 volumes. I have created a nifti file in my own fMRI program.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |