Sentence examples for passenger vectors from inspiring English sources

Table 6 shows an example of passenger vectors from Table 5 with the corresponding target variable.

Now to make a new prediction on a passenger vector or a number of passenger vectors, we input it in the model and the model will return a number from 0 to 1 for each passenger vector.

At this point a cluster label ("A", "B" or "C") is assigned to each passenger vector.

Every passenger vector will be assigned to the "closest" cluster center as defined by Euclidean distance in n-dimensions, where n is the number of features of the vectors.

The system works in the following way: For each passenger a vector is constructed considering data pertaining to the D period only.

Then if a vector (passenger) belongs to "A", its Cluster_k3_A feature is set to 1 and 0 otherwise, conversely, if the vector belongs to "B" then only Cluster_k3_B is set to 1, and so on.

In order to investigate the wake structure around the external rear view mirror of a passenger car, velocity vector fields and spectra of velocity fluctuations in the mirror wake were measured by hot wire anemometry and laser Doppler velocimetry in a blow down wind tunnel at Re=200,000.

Now for each passenger we construct a vector that depends on three variables: 1) The passenger data in the Tables.

Once the vectors for each passenger are generated we need to define the target variable.

The former vectors facilitate integration of passenger genes into attB sites while the latter allows creation of new attB sites.

Specifically, a temporally varying structure of subway passenger variability cannot be captured by a single EOF loading vector; several EOF loading vectors are required to describe it.