The Gradient and Directional Derivative

Introduction: Slope: In vector calculation, the gradient is considered a vector field in the function. It is a point on the path of the maximum growth rate of the scalar field. That size is the maximum correction rate. Direction Derivative: Directional derivative represents the instantaneous correction rate of a function. We summarize the perspective of partial derivatives. Gradient: Define the slope of the function f (x, y) as follows. This is a vector operator r as a scalar function f (x, y) = [(delf) / (delx) x, y) in the same way.

Briefly, the derivative faces the fastest rising. The good thing is that the gradations are exactly the same. With one exception, Gradient is a vector value function that stores partial derivatives. In other words, the gradient is a vector, each of which is a partial derivative of a particular variable. This collection contains 781 data records from which you can download in CSV format. Of the eight available functions, we focus only on the size and price, for clarity. For each 781 record, the size (square foot) becomes the input factor and the price becomes the target value.

The gradient is just a vector, which is a multivariate generalization of the derivative (dy / dx), the instantaneous rate of change of y with respect to x. The difference is that Gradient replaces that position to calculate the derivative of a function that depends on multiple variables or multiple variables. The slope is calculated using partial derivatives. Another major difference between gradients and derivatives is that the slope of the function produces a vector field.

In the HOG feature quantity descriptor, the distribution (direction gradient) (direction gradient) (histogram) in the gradient direction is used as the feature quantity. Image gradation (derivative of x and y) is useful because it is known that the gradation around edges and corners (areas where sudden intensity changes) is large and there is more information on the shape of the object than the flat . Area Print ('Test Accuracy SVC =', round (svc.score (X_test, y_test), 4)) reads and extracts images of cars and cars using the above feature extraction method. These values ​​are stored in car_features and notcar_features. The output of carfeatures is 1, notcar_features is 0. The 'y' value is set based on this assumption. 'x' is a combination of car_features list and notcar_ features list.