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Cost function: quadratic, cross entropy, log likelihood (course: refining numbers into gold) quadratic cost function C = 1 2 n ...

Classification algorithm - logistic regression [also an iterative method, self updating w] Linear input to classification problem: Input: the formula of linear regression is used as the input of logical regression sigmoid function: Logistic regression formula: [that is, how sigmoid converts input into probability value] e: 2.71 Z = ...

1, ndarray object 1. Install Numpy: on the command line, enter pip install numpy 2.ndarray object -- a core data structure around Numpy The ndarray object is an N-dimensional array. But note that ndarray is homogeneous. Homogeneity means that all elements in an N-dimensional array must belong to the same data type. PS: list s in Python are het ...

Kalman filter for tracking and prediction of moving targets on images brief introduction Kalman filter predicts the current state through the previous state and uses the current observation state for correction The direct measurements are left, top, right and bottom, representing the upper left and lower right coordinates of the targe ...

Decision tree in sklearn Module: sklearn.tree tree.DecisionTreeClassifierClassification treetree.DecisionTreeRegressorRegression treetree.export_graphvizThe generated decision tree is exported to DOT mode for drawingtree.ExtraTreeClassifierHigh random version classification treetree.ExtraTreeRegressorHigh random version of regression tr ...

See the end of the text for the code 1. Purpose and content of the experiment 1.1 experimental purpose Three line segment generation algorithms (DDA, improved Bresenham line drawing algorithm and improved midpoint line drawing algorithm) are realized through Python language, the core ideas of the three algorithms are deeply understood, ...

This tutorial is open source: GitHub Welcome fork preface In this tutorial, we will describe how to use KdTree to find the K nearest neighbors of a specific point or location, and how to find all neighbors within a radius specified by the user (random in this case). Introduction to theory kd tree or k-dimensional tree is a data struct ...

1, Numpy advantage 1. Introduction to ndarray NumPy provides an N-dimensional array type ndarray, which describes a collection of "items" of the same type. Store with ndarray: import numpy as np # Create ndarray score = np.array( [[80, 89, 86, 67, 79], [78, 97, 89, 67, 81], [90, 94, 78, 67, 74], [91, 91, 90, 67, 69], [76, 87, 75, ...

Catalog 1. Decision Tree Model Data Classification 2. Decision tree pruning alleviates over-fitting problems *Common decision tree algorithms are ID3, C4.5, and CART. The ID3 algorithm, proposed by Quinlan, an Australian computer scientist, in 1986, is one of the classic decision tree algorithms. The ID3 algorithm uses information gain to s ...

The reason for writing this article is that after embedding with SVD and deepwall in a recommended task, the effect of the model has been improved, and the application of SVD is beyond the knowledge of dimension reduction and there is a lot to think about, so some methods of SVD and embedding are summarized. 1. Singular Value Decomposition SVD ...