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To expand the shape of an array, use the numpy.expand_dims() method. Insert a new array that will appear at the axis position in the expanded array shape. The function returns the View of the input array with the number of dimensions increased.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.StepsAt first, import the required library −import numpy as npCreating an array using the array() method −arr = np.array([5, 10, 15, 20, 25, 30])Display the ... Read More
To unpack elements of a uint8 array into a binary-valued output array, use the numpy.unpackbits() method in Python Numpy. The result is binary-valued (0 or 1). The axis is the dimension over which bit-unpacking is done. The axis is set using the "axis" parameter.Each element of the input array represents a bit-field that should be unpacked into a binary-valued output array. The shape of the output array is either 1-D (if axis is None) or the same shape as the input array with unpacking done along the axis specified.The axis is the dimension over which bit-unpacking is done. None implies ... Read More
To pack the elements of a binary-valued array into bits in a uint8 array, use the numpy.packbits() method in Python Numpy. The result is padded to full bytes by inserting zero bits at the end. The axis is set using the axis parameter. The axis is the dimension over which bit-packing is done. We have set axis 1.The axis is the dimension over which bit-packing is done. None implies packing the flattened array. The bitorder is the order of the input bits. ‘big’ will mimic bin(val), [0, 0, 0, 0, 0, 0, 1, 1] ⇒ 3 = 0b00000011, 'little' will ... Read More
To pack the elements of a binary-valued array into bits in a uint8 array, use the numpy.packbits() method in Python Numpy. The result is padded to full bytes by inserting zero bits at the end. The axis is set using the axis parameter. The axis is the dimension over which bit-packing is done.The axis is the dimension over which bit-packing is done. None implies packing the flattened array. The bitorder is the order of the input bits. ‘big’ will mimic bin(val), [0, 0, 0, 0, 0, 0, 1, 1] ⇒ 3 = 0b00000011, 'little' will reverse the order so [1, ... Read More
STING stands for Statistical Information Grid. STING is a grid-based multiresolution clustering method in which the spatial area is divided into rectangular cells. There are several methods of such rectangular cells equivalent to multiple methods of resolution, and these cells form a hierarchical structure each cell at a high level is separation to form several cells at the next lower level.Statistical data regarding the attributes in each grid cell (including the mean, maximum, and minimum values) is precomputed and stored. Statistical parameters of higher-level cells can simply be calculated from the parameters of the lower-level cells.These parameters contain the following ... Read More
Clustering is the significant data mining approaches for knowledge discovery. The clustering is an exploratory data analysis methods that categorizes several data objects into same groups, such as clusters.DENCLUE represents Density-based Clustering. It is a clustering approach depends on a group of density distribution functions. The DENCLUE algorithm use a cluster model depends on kernel density estimation. A cluster is represented by a local maximum of the predicted density function.DENCLUE doesn't operate on records with uniform distribution. In high dimensional space, the data always look like uniformly distributed because of the curse of dimensionality. Hence, DENCLUDE doesn't operate well on ... Read More
To compute the bit-wise NOT of a boolean array, use the numpy.bitwise_not() method in Python Numpy. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ˜.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.StepsAt first, import ... Read More
To compute the bit-wise OR of two 1D arrays element-wise, use the numpy.bitwise_xor() method in Python Numpy. Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ^.The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original ... Read More
DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise. It is a density based clustering algorithm. The algorithm increase regions with sufficiently high density into clusters and finds clusters of arbitrary architecture in spatial databases with noise. It represents a cluster as a maximum group of density-connected points.The concept of density-based clustering includes a number of new definitions as follows −The neighborhood within a radius ε of a given object is known as the εneighborhood of the object.If the ε-neighborhood of an object includes at least a minimum number, MinPts, of objects, then the object is known as core ... Read More
ROCK stands for Robust Clustering using links. It is a hierarchical clustering algorithm that analyze the concept of links (the number of common neighbours among two objects) for data with categorical attributes. It display that such distance data cannot lead to high-quality clusters when clustering categorical information.Moreover, most clustering algorithms create only the similarity among points when clustering i.e., at each step, points that are combined into a single cluster. This “localized” method is prone to bugs. For instance, two distinct clusters can have a few points or outliers that are near; thus, relying on the similarity among points to ... Read More