numpy.greater (x1, x2 [, out]): checks if x1 is greater than x2 or not.
x1, x2: [array_like] Input arrays. If x1.shape! = X2.shape, they must be broadcastable to a common shape out: [ndarray, boolean] Array of bools, or a single bool if x1 and x2 are scalars.
Boolean array indicating results, whether x1 is greater than x2 or not.
Not equal: [True False] Not equal: [[True False] [True False]] Is a greater than b: [False False]
Code 2: strong>
Comparing float with int: [False True] Comparing float with int using .greater (): [True False] pre>
Comparing complex with int: [True False] Comparing complex with int using .greater (): [False False] pre >
These codes will not work for online IDs. Please run them on your systems to see how they work
This article is provided by Mohit Gupta_OMG
This is the first book on synthetic data for deep learning, and its extensive coverage could make this book the standard benchmark for synthetic data for years to come. The book can also serve as an i...
The ability to identify patterns is an essential component of sensory intelli- gent machines. Pattern recognition is therefore an indispensible component of the so-called “Intelligent Control System...
The role of adaptation, learning and optimization are becoming increasingly essen- tial and intertwined. The capability of a system to adapt either through modification of its physiological structure ...
The genesis of this book began in 2012. Hadoop was being explored in mainstream organizations, and we believed that information architecture was about to be transformed. For many years, business intel...