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at August 14, 2009 02:22 by atr000
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if bootstraps == 1: srri = lambda low, high, size: range(size) else: srri = scipy.random.random_integers for boot in range(bootstraps): for r in range(n1): for c in range(n0): sample_size = trial_result[r][c].size choices = srri(0, sample_size-1, sample_size) meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean() model_grid[:,:,:,boot], params[:,boot] = \ process_grid(s0, s1, meas_grid) #Instead of srri = scipy.random.random_integers if bootstraps == 1: for r in range(n1): for c in range(n0): meas_grid[r,c] = pylab.array(trial_result[r][c],dtype=float).mean() model_grid[:,:,:,boot], params[:,boot] = \ process_grid(s0, s1, meas_grid) else: for boot in range(bootstraps): for r in range(n1): for c in range(n0): sample_size = trial_result[r][c].size choices = srri(0, sample_size-1, sample_size) meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean() model_grid[:,:,:,boot], params[:,boot] = \ process_grid(s0, s1, meas_grid) #OR srri = scipy.random.random_integers for boot in range(bootstraps): if bootstraps == 1: for r in range(n1): for c in range(n0): meas_grid[r,c] = pylab.array(trial_result[r][c],dtype=float).mean() model_grid[:,:,:,boot], params[:,boot] = \ process_grid(s0, s1, meas_grid) else: for r in range(n1): for c in range(n0): sample_size = trial_result[r][c].size choices = srri(0, sample_size-1, sample_size) meas_grid[r,c] = pylab.array(trial_result[r][c][choices],dtype=float).mean() model_grid[:,:,:,boot], params[:,boot] = \ process_grid(s0, s1, meas_grid)
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from activestate. not complete or tested but good ref
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python lambda example
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python
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Python