scale

问题 I am new to React. I am working on a project, whose one of the features is dragging and dropping The draggable components are supposed to be dragged and dropped on the container, say A.container , A also has the feature of zoom in and zoom out. I have achieved this feature by changing its scale accordingly to achieve zoom in and zoom out. To achieve zoom in, I am multiplying the original scale which is 1 by 2. To achieve zoom out, I am dividing the current scale by 2. The dropped component is

问题 I have a dataset, say Data, which consists of categorical and numerical variables. After cleaning them, I have scaled only the numerical variables (guess catgorical must not be scaled) using Data <- Data %>% dplyr::mutate_if(is.numeric, ~scale(.) %>% as.vector) I then split it randomly to 70-30 percentage using set.seed(123) sample_size = floor(0.70*nrow(Data)) xyz <- sample(seq_len(nrow(Data)),size = sample_size) Train_Set <- Join[xyz,] Test_Set <- Join[-xyz,] I have built a classification

问题 I am currently moving my data analysis from R to Python. When scaling a dataset in R i would use R.scale(), which in my understanding would do the following: (x-mean(x))/sd(x) To replace that function I tried to use sklearn.preprocessing.scale(). From my understanding of the description it does the same thing. Nonetheless I ran a little test-file and found out, that both of these methods have different return-values. Obviously the standard deviations are not the same... Is someone able to

问题 I am currently moving my data analysis from R to Python. When scaling a dataset in R i would use R.scale(), which in my understanding would do the following: (x-mean(x))/sd(x) To replace that function I tried to use sklearn.preprocessing.scale(). From my understanding of the description it does the same thing. Nonetheless I ran a little test-file and found out, that both of these methods have different return-values. Obviously the standard deviations are not the same... Is someone able to

问题 library(tidyverse) df <- tibble(col1 = rep(c("A", "B"), 2), col2 = c(0.4, 0.7, 3, 9), col3 = c("I", "I", "II", "II")) #> # A tibble: 4 x 3 #> col1 col2 col3 #> <chr> <dbl> <chr> #> 1 A 0.4 I #> 2 B 0.7 I #> 3 A 3 II #> 4 B 9 II ggplot(df, aes(col1, col2)) + geom_col() + facet_wrap(vars(col3), scales = "free") I want to create integer breaks for the ggplot above such that: There is an integer lower break at or below the lowest value value for each facet. There is an integer upper break at or

问题 library(tidyverse) df <- tibble(col1 = rep(c("A", "B"), 2), col2 = c(0.4, 0.7, 3, 9), col3 = c("I", "I", "II", "II")) #> # A tibble: 4 x 3 #> col1 col2 col3 #> <chr> <dbl> <chr> #> 1 A 0.4 I #> 2 B 0.7 I #> 3 A 3 II #> 4 B 9 II ggplot(df, aes(col1, col2)) + geom_col() + facet_wrap(vars(col3), scales = "free") I want to create integer breaks for the ggplot above such that: There is an integer lower break at or below the lowest value value for each facet. There is an integer upper break at or

问题 I have three data frames that are each scaled individually with MinMaxScaler(). def scale_dataframe(values_to_be_scaled) values = values_to_be_scaled.astype('float64') scaler = MinMaxScaler(feature_range=(0, 1)) scaled = scaler.fit_transform(values) return scaled scaled_values = [] for i in range(0,num_df): scaled_values.append(scale_dataframe(df[i].values)) The problem I am having is that each dataframe gets scaled according to its own individual set of column min and max values. I need all

问题 I'm trying to plot an ESRI Grid as a raster image of a surface. I've figured out how to make the plot, but not how to control R's color scale. # open necessary libraries library("raster") library("rgdal") library("ncdf") # goal: select an ESRI Grid ASCII file and plot it as an image. infile <- file.choose("Results") r <- raster(infile) # read in metadata from ESRI output file, split up into relevant variables info <- read.table(infile, nrows=6) NCOLS <- info[1,2] NROWS <- info[2,2] XLLCORNER

问题 Update: I've created this Code Page demo to show what I'm talking about. This demo shows two methods, the first is the brute-force method and the second is Robert McKee's method. With the first method, press the Scroll to End button to derive the vertical (y) scaling value, which is then shown next to the button that was just pushed. The list of values below the buttons, you will also see the number of 1px scrolls horizontally and vertically needed to fully scroll to the lower-left corner of

问题 Update: I've created this Code Page demo to show what I'm talking about. This demo shows two methods, the first is the brute-force method and the second is Robert McKee's method. With the first method, press the Scroll to End button to derive the vertical (y) scaling value, which is then shown next to the button that was just pushed. The list of values below the buttons, you will also see the number of 1px scrolls horizontally and vertically needed to fully scroll to the lower-left corner of