prediction | 易学教程

Setting an upper bound of 0 on a 3d loess smoothing with negative values in R

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问题 I have a bit of a bizarre question, but hoping someone can help me. I am attempting to create a surface plot of the bottom of a lake and then add some points showing plant frequency for a visual of where aquatic plants are occurring throughout the lake. Right now I am working on creating the surface plot in both scatterplot3d and wireframe using the scatterplot3d and lattice packages, respectively, in R. In order to achieve the type of plot I am interested in I have converted the depths to

Function for testing system stability, which receives predicted time series as input

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问题 I want to write a function that gets a time series and a standard deviation as parameters and returns an adjusted time series which looks like a forecast. With this function I want to test a system for stability, which gets a forecasted time series list for weather as input parameter. My approach for such a function, which is described below: vector<tuple<datetime, double>> get_adjusted_timeseries(vector<tuple<datetime, double>>& timeseries_original, const double stddev, const double dist_mid

Using glmnet to predict a continuous variable in a dataset

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问题 I have this data set. wbh I wanted to use the R package glmnet to determine which predictors would be useful in predicting fertility. However, I have been unable to do so, most likely due to not having a full understanding of the package. The fertility variable is SP.DYN.TFRT.IN. I want to see which predictors in the data set give the most predictive power for fertility. I wanted to use LASSO or ridge regression to shrink the number of coefficients, and I know this package can do that. I'm

Predictive dictionary text input API/framework for with iOS apps [closed]

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问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed last year . I have been looking for while for an 'predictive dictionary' that is used the iPhone and other other phones to increase text input accuracy by suggesting the words. Does anyone know if such dictionary is available for Objective C/iOS? Or a way/tools to create one from scratch? 回答1: Did you consider using

CNTK python API: How to get predictions from the trained model?

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问题 I have a trained model which I am loading using CNTK.load_model() function. I was looking at the MNIST Tutorial on the CNTK git repo as reference for model evaluation code. I have created a data reader (which is a MinibatchSource object) and trying to run model.eval(mb) where mb = minibatch_source.next_minibatch(...) (Similar to this answer) But, I'm getting the following error message Traceback (most recent call last): File "LID_test.py", line 162, in <module> test_and_evaluate() File "LID

Prediction in R - GLMM

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问题 When we use a traditional logistic regression and make a prediction in R, for example: library(dplyr) n = 300 xx<-c("r1","r2","r3","r4","r5") xxx<-c("e1","e2","e3") p=0.3 df1 <- data_frame( xx1 = runif(n, min = 0, max = 10), xx2 = runif(n, min = 0, max = 10), xx3 = runif(n, min = 0, max = 10), School = factor(sample(xxx, n,re=TRUE)), Rank = factor(sample(xx, n,re=TRUE)), yx = as.factor(rbinom(n, size = 1, prob = p)) ) df1 mm<-glm(yx ~ xx1 + xx2 + xx3 + School + Rank,binomial,df1) n11 = data

Error in predicting raster with randomForest, Caret, and factor variables

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问题 I am trying to predict a raster layer with randomForest and the caret package, but fail when I introduce factor variables. Without factors, everything works fine, but as soon as I bring a factor in, I get the error: Error in predict.randomForest(modelFit, newdata) : Type of predictors in new data do not match that of the training data. I have created some sample code below that walks through he process. I present it in a few steps for transparency and to provide a working example. (To skip

Prediction using InceptionV3 in Tensorflow

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问题 I have trained InceptionV3 model in tensor flow on my own dataset. I have the checkpoint file and the graph (.meta) from training. I am using these files to classify the label of a new image. Till now I have the following: inception_v3 code from TFslim def inception_v3(inputs, dropout_keep_prob=0.8, num_classes=1000, is_training=True, restore_logits=True, scope=''): """Latest Inception from http://arxiv.org/abs/1512.00567. "Rethinking the Inception Architecture for Computer Vision" Christian

Time series prediction using support vector regression

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问题 I've been trying to implement time series prediction tool using support vector regression in python language. I use SVR module from scikit-learn for non-linear Support vector regression. But I have serious problem with prediction of future events. The regression line fits the original function great (from known data) but as soon as I want to predict future steps, it returns value from the last known step. My code looks like this: import numpy as np from matplotlib import pyplot as plt from

Training an LSTM neural network to forecast time series in pybrain, python

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I have a neural network created using PyBrain and designed to forecast time series. I am using the sequential dataset function, and trying to use a sliding window of 5 previous values to predict the 6th. One of my problems is that I can't figure out how to create the required dataset by appending the 5 previous values to the inputs and the 6th as an output. I am also unsure of how exactly to forecast values in the series once the network is trained. Posting my code below: from pybrain.datasets import SupervisedDataSet from pybrain.datasets import SequentialDataSet from pybrain.tools.shortcuts