1) 巧用 where函数 where函数是numpy的内置,也是一个非常有用的函数,提供了快速并且灵活的计算功能。 def f_norm_1(data, estimate): residule = 0 for row_index in range(data.shape[0]): for column_index in range(data.shape[1]): if data[row_index][column_index] != 0: residule += (data...
def vector_angle(u, v, direction=None): ''' vector_angle(u, v) yields the angle between the two vectors u and v. The optional argument direction is by default None, which specifies that the smallest possible angle between the vectors be reported; if the vectors u and v are 2D vectors...
Solution 1: Correlation quantifies the degree of correspondence between two vectors as they vary. However, if one vector remains constant, it is impossible to observe their mutual variation. The formula for Pearson's product-moment correlation coefficient involves dividing the covariance ofXandYby the...
Consider the angle shown in :numref:`fig_angle`. The dot product :eqref:`eq_dot_def` also admits a geometric interpretation: it is closely related to the angle between two vectors. Consider the angle shown in :numref:`fig_angle`. ![Between any two vectors in the plane there is a ...
Parameters --- X : {array-like, sparse matrix}, shape = [n_samples, n_features] Training vectors, where n_samples is the number of samples and n_features is the number of features. Returns --- maj : array-like, shape = [n_samples] Predicted class labels. """ if self.voting ==...
vecmag- Magnitude of Vectors. vecmag2- Squared Magnitude of the vectors. vecangle- Alternative to atan2 (0 <= angle <= 2*pi). vecdot- Dot product of two vectors. veccross- Cross product of two vectors. vecrotx- Rotation matrix around the x-axis. ...
The input is given in the form of astructured arrayin numpy. The first four fields of the input arrayvectorsmust be either: np.dtype([('pT','f8'), ('eta','f8'), ('phi','f8'), ('mass','f8')]) or ifcluster(..., ep=True): ...
def l2_batch_normalize(x, epsilon=1e-12, scope=None): """ Helper function to normalize a batch of vectors. :param x: the input placeholder :param epsilon: stabilizes division :return: the batch of l2 normalized vector """ with tf.name_scope(scope, "l2_batch_normalize") as scope: x...
# intersect manifold with vantage vectors # calculate distance dist_A_alpha = dist_A dist_B_alpha = dist_B # calculate lambda # sinusoid taking as input the angle between vantage and line AB (separating the two targets) line_AB = np3_to_vector3(PAB).perpendicular() angle_A = py_ang...
These convert between the standard 3-d vector representation and their equivalent quaternions, which allows them to be manipulated as vectors — as inR * from_vector_part(v) * R.conjugate(). However, note that you may not need to convert to/from quaternions. For example, to rotate vectorsv...