#Find the range of a NumPy array's elements by usingnumpy.max()andnumpy.min() You can also use thenumpy.max()andnumpy.min()methods to find the range of a NumPy array's elements. main.py importnumpyasnp arr=np.a
mean(lines, axis=0, dtype=np.int32) line = tuple(map(tuple, line)) # make sure it's tuples not numpy array for cv2.line to work return line left_line = mean_line(left_line, self.left_lines) right_line = mean_line(right_line, self.right_lines) return draw_lane_lines(image, ...
openfoam_invar_numpy,output_names=["nu"],metrics={"mean_nu":lambdavar:torch.mean(var["nu"])},nodes=nodes,)domain.add_monitor(monitor)monitor=PointwiseMonitor(openfoam_invar_numpy,output_names=["D"],metrics={"mean_D":lambdavar:torch.mean(var["D"])},nodes=nodes,)domain.add_monitor(mo...
import numpy as np import pandas as pd outliers=[] def detect_outlier(data_1): threshold=3 mean_1 = np.mean(data_1) std_1 =np.std(data_1) for y in data_1: z_score= (y - mean_1)/std_1 if np.abs(z_score) > threshold: outliers.append(y) return outliers we now pass data...
x_diff = np.array([x_i-xy_mean[0] for x_i in x]) y_diff = np.array([y_i-xy_mean[1] for y_i in y]) diff_xy = np.transpose([x_diff,y_diff]) md = [] for i in range(len(diff_xy)): md.append(np.sqrt(np.dot(np.dot(np.transpose(diff_xy[i]),inv_covariance_xy...
For setting the prior mean function: when calling fit(), pass in a vector mu0 that is the same size as item_features. Each entry of mu0 is the prior preference function mean for the corresponding item in item_features when calling predict_f() to predict the score for test items, or ...
I don’t know what you mean by “more robust”. If I understand the method he is using correctly, then it minimizes the RMS error between corresponding points from either point cloud. It can accept any pair of ordered point clouds (of the same size), regardless how similar they are to...
import numpy def pca(data, algorithm='eig'): """pca(data) -> mean, pcs, norm_pcs, variances, positions, norm_positions Perform Principal Components Analysis on a set of n data points in k dimensions. The data array must be of shape (n, k). ...
(image, lines)defmean_line(line, lines):iflineisnotNone: lines.append(line)iflen(lines)>0: line = np.mean(lines, axis=0, dtype=np.int32) line = tuple(map(tuple, line))# make sure it's tuples not numpy array for cv2.line to workreturnline left_line = mean_line(left_line, ...
(lines, axis=0, dtype=np.int32) line = tuple(map(tuple, line))# make sure it's tuples not numpy array for cv2.line to workreturnline left_line = mean_line(left_line, self.left_lines) right_line = mean_line(right_line, self.right_lines)returndraw_lane_lines(image, (left_line,...