The emergence of real-time auction in online advertising has drawn huge attention of modeling the market competition, i.e., bid landscape forecasting. The problem is formulated as to forecast the probability distribution of market price for each ad auction. With the consideration of the censorship issue which is caused by the second-price auction mechanism, many researchers have devoted their efforts on bid landscape forecasting by incorporating survival analysis from medical research field. However, most existing solutions mainly focus on either counting-based statistics of the segmented sample clusters, or learning a parameterized model based on some heuristic assumptions of distribution forms. Moreover, they neither consider the sequential patterns of the feature over the price space. In order to capture more sophisticat
| import Foundation | |
| import TensorFlow | |
| import Python | |
| PythonLibrary.useVersion(3, 6) | |
| let np = Python.import("numpy") | |
| let (x_train, y_train) = readMNIST(imagesFile: "Resources/train-images.idx3-ubyte", | |
| labelsFile: "Resources/train-labels.idx1-ubyte") |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torch.utils.data import TensorDataset, DataLoader | |
| import matplotlib.pyplot as plt | |
| X = torch.tensor([ | |
| [i] for i in np.arange(1, 4.1, 0.1) |
| import time | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torch.utils.data import DataLoader | |
| from torchvision import datasets, transforms | |
| torch.backends.cudnn.benchmark = True |
| from collections import Counter | |
| import numpy as np | |
| class SlidingWindow: | |
| def __init__(self, window_size, overlap_rate=0.5): | |
| self.window_size = window_size | |
| assert 0.0 < overlap_rate and overlap_rate <= 1.0 | |
| self.overlap_rate = overlap_rate | |
| self.overlap = int(window_size * overlap_rate) |
| #!/bin/zsh | |
| function fizzbuzz () { | |
| if [ $(( $1 % 15 )) -eq 0 ]; then | |
| echo FizzBuzz | |
| elif [ $(( $1 % 3 )) -eq 0 ]; then | |
| echo Fizz | |
| elif [ $(( $1 % 5 )) -eq 0 ]; then | |
| echo Buzz | |
| else |
| from ... import Tensor | |
| from .. import Parameter | |
| from .module import Mudule | |
| from typinh import Any, Optional | |
| """ | |
| add this code to | |
| https://github.com/pytorch/pytorch/blob/ff7921e85bad0ad47bc7fa6d48c2f8762cf3f6b3/torch/nn/modules/__init__.pyi.in#L2-L6 |
| import os | |
| import time | |
| import tqdm | |
| import torch | |
| import torch.nn as nn | |
| from torch.utils.data import DataLoader | |
| class NeuralNetworkClassifier: | |
| def __init__(self, model, criterion, optimizer, optimizer_config: dict, experiment) -> None: |
Sufficient physical activity and restful sleep play a major role in the prevention and cure of many chronic conditions. Being able to proactively screen and monitor such chronic conditions would be a big step forward for overall health.
Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations between their readings. Forecasting geo-sensory time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatio-temporal correlations and external factors. In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors' readings, meteorological data, and spatial data. More specifically, our model consists of two major parts: 1) a multi-level attention mechanism to model the dynamic spatio-te