Small Change in R Coding Style

This PR is a  very mall change, but can help make the coding style consistent. I used "<-" instead of "=" as it's more commonly recommended.

R-package/vignettes/classifyRealImageWithPretrainedModel.Rmd

@@ -35,13 +35,13 @@ Make sure you unzip the pre-trained model in current folder. And we can use the
 loading function to load the model into R.
 
 ```{r}
-model = mx.model.load("Inception/Inception_BN", iteration=39)
+model <- mx.model.load("Inception/Inception_BN", iteration = 39)
 ```
 
 We also need to load in the mean image, which is used for preprocessing using ```mx.nd.load```.
 
 ```{r}
-mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
+mean.img <- as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
 ```
 
 Load and Preprocess the Image
@@ -52,7 +52,7 @@ from imager package. But you can always change it to other images.
 Load and plot the image:
 
 ```{r, fig.align='center'}
-im <- load.image(system.file("extdata/parrots.png", package="imager"))
+im <- load.image(system.file("extdata/parrots.png", package = "imager"))
 plot(im)
 ```
 
@@ -64,7 +64,7 @@ Because mxnet is deeply integerated with R, we can do all the processing in R fu
 The preprocessing function:
 
 ```{r}
-preproc.image <-function(im, mean.image) {
+preproc.image <- function(im, mean.image) {
   # crop the image
   shape <- dim(im)
   short.edge <- min(shape[1:2])
@@ -77,7 +77,7 @@ preproc.image <-function(im, mean.image) {
   resized <- resize(croped, 224, 224)
   # convert to array (x, y, channel)
   arr <- as.array(resized)
-  dim(arr) = c(224, 224, 3)
+  dim(arr) <- c(224, 224, 3)
   # substract the mean
   normed <- arr - mean.img
   # Reshape to format needed by mxnet (width, height, channel, num)
@@ -98,7 +98,7 @@ Now we are ready to classify the image! We can use the predict function
 to get the probability over classes.
 
 ```{r}
-prob <- predict(model, X=normed)
+prob <- predict(model, X = normed)
 dim(prob)
 ```
 

R-package/vignettes/fiveMinutesNeuralNetwork.Rmd

@@ -19,14 +19,14 @@ First of all, let us load in the data and preprocess it:
 require(mlbench)
 require(mxnet)
 
-data(Sonar, package="mlbench")
-
-Sonar[,61] = as.numeric(Sonar[,61])-1
-train.ind = c(1:50, 100:150)
-train.x = data.matrix(Sonar[train.ind, 1:60])
-train.y = Sonar[train.ind, 61]
-test.x = data.matrix(Sonar[-train.ind, 1:60])
-test.y = Sonar[-train.ind, 61]
+data(Sonar, package = "mlbench")
+
+Sonar[,61] <- as.numeric(Sonar[,61])-1
+train.ind <- c(1:50, 100:150)
+train.x <- data.matrix(Sonar[train.ind, 1:60])
+train.y <- Sonar[train.ind, 61]
+test.x <- data.matrix(Sonar[-train.ind, 1:60])
+test.y <- Sonar[-train.ind, 61]
 ```
 
 Next we are going to use a multi-layer perceptron as our classifier. In `mxnet`, we have a function called `mx.mlp` so that users can build a general multi-layer neural network to do classification or regression.
@@ -59,8 +59,8 @@ graph.viz(model$symbol$as.json())
 ```
 
 ```{r}
-preds = predict(model, test.x)
-pred.label = max.col(t(preds))-1
+preds <- predict(model, test.x)
+pred.label <- max.col(t(preds)) - 1
 table(pred.label, test.y)
 ```
 
@@ -73,11 +73,11 @@ Again, let us preprocess the data first.
 ```{r}
 data(BostonHousing, package="mlbench")
 
-train.ind = seq(1, 506, 3)
-train.x = data.matrix(BostonHousing[train.ind, -14])
-train.y = BostonHousing[train.ind, 14]
-test.x = data.matrix(BostonHousing[-train.ind, -14])
-test.y = BostonHousing[-train.ind, 14]
+train.ind <- seq(1, 506, 3)
+train.x <- data.matrix(BostonHousing[train.ind, -14])
+train.y <- BostonHousing[train.ind, 14]
+test.x <- data.matrix(BostonHousing[-train.ind, -14])
+test.y <- BostonHousing[-train.ind, 14]
 ```
 
 Although we can use `mx.mlp` again to do regression by changing the `out_activation`, this time we are going to introduce a flexible way to configure neural networks in `mxnet`. The configuration is done by the "Symbol" system in `mxnet`, which takes care of the links among nodes, the activation, dropout ratio, etc. To configure a multi-layer neural network, we can do it in the following way:
@@ -108,7 +108,7 @@ model <- mx.model.FeedForward.create(lro, X=train.x, y=train.y,
 It is also easy to make prediction and evaluate
 
 ```{r}
-preds = predict(model, test.x)
+preds <- predict(model, test.x)
 sqrt(mean((preds-test.y)^2))
 ```
 

doc/R-package/classifyRealImageWithPretrainedModel.md

@@ -73,14 +73,14 @@ loading function to load the model into R.
 
 
 ```r
-model = mx.model.load("Inception/Inception_BN", iteration=39)
+model <- mx.model.load("Inception/Inception_BN", iteration=39)
 ```
 
 We also need to load in the mean image, which is used for preprocessing using ```mx.nd.load```.
 
 
 ```r
-mean.img = as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
+mean.img <- as.array(mx.nd.load("Inception/mean_224.nd")[["mean_img"]])
 ```
 
 Load and Preprocess the Image
@@ -92,7 +92,7 @@ Load and plot the image:
 
 
 ```r
-im <- load.image(system.file("extdata/parrots.png", package="imager"))
+im <- load.image(system.file("extdata/parrots.png", package = "imager"))
 plot(im)
 ```
 
@@ -107,7 +107,7 @@ The preprocessing function:
 
 
 ```r
-preproc.image <-function(im, mean.image) {
+preproc.image <- function(im, mean.image) {
   # crop the image
   shape <- dim(im)
   short.edge <- min(shape[1:2])
@@ -120,7 +120,7 @@ preproc.image <-function(im, mean.image) {
   resized <- resize(croped, 224, 224)
   # convert to array (x, y, channel)
   arr <- as.array(resized)
-  dim(arr) = c(224, 224, 3)
+  dim(arr) <- c(224, 224, 3)
   # substract the mean
   normed <- arr - mean.img
   # Reshape to format needed by mxnet (width, height, channel, num)
@@ -143,7 +143,7 @@ to get the probability over classes.
 
 
 ```r
-prob <- predict(model, X=normed)
+prob <- predict(model, X = normed)
 dim(prob)
 ```
 

doc/R-package/fiveMinutesNeuralNetwork.md

@@ -34,14 +34,14 @@ require(mxnet)
 ```
 
 ```r
-data(Sonar, package="mlbench")
+data(Sonar, package = "mlbench")
 
-Sonar[,61] = as.numeric(Sonar[,61])-1
-train.ind = c(1:50, 100:150)
-train.x = data.matrix(Sonar[train.ind, 1:60])
-train.y = Sonar[train.ind, 61]
-test.x = data.matrix(Sonar[-train.ind, 1:60])
-test.y = Sonar[-train.ind, 61]
+Sonar[,61] <- as.numeric(Sonar[,61])-1
+train.ind <- c(1:50, 100:150)
+train.x <- data.matrix(Sonar[train.ind, 1:60])
+train.y <- Sonar[train.ind, 61]
+test.x <- data.matrix(Sonar[-train.ind, 1:60])
+test.y <- Sonar[-train.ind, 61]
 ```
 
 Next we are going to use a multi-layer perceptron as our classifier. In `mxnet`, we have a function called `mx.mlp` so that users can build a general multi-layer neural network to do classification or regression.
@@ -102,15 +102,15 @@ graph.viz(model$symbol$as.json())
 [<img src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/knitr/graph.computation.png">](https://github.com/dmlc/mxnet)
 
 ```r
-preds = predict(model, test.x)
+preds <- predict(model, test.x)
 ```
 
 ```
 ## Auto detect layout of input matrix, use rowmajor..
 ```
 
 ```r
-pred.label = max.col(t(preds))-1
+pred.label <- max.col(t(preds)) - 1
 table(pred.label, test.y)
 ```
 
@@ -131,11 +131,11 @@ Again, let us preprocess the data first.
 ```r
 data(BostonHousing, package="mlbench")
 
-train.ind = seq(1, 506, 3)
-train.x = data.matrix(BostonHousing[train.ind, -14])
-train.y = BostonHousing[train.ind, 14]
-test.x = data.matrix(BostonHousing[-train.ind, -14])
-test.y = BostonHousing[-train.ind, 14]
+train.ind <- seq(1, 506, 3)
+train.x <- data.matrix(BostonHousing[train.ind, -14])
+train.y <- BostonHousing[train.ind, 14]
+test.x <- data.matrix(BostonHousing[-train.ind, -14])
+test.y <- BostonHousing[-train.ind, 14]
 ```
 
 Although we can use `mx.mlp` again to do regression by changing the `out_activation`, this time we are going to introduce a flexible way to configure neural networks in `mxnet`. The configuration is done by the "Symbol" system in `mxnet`, which takes care of the links among nodes, the activation, dropout ratio, etc. To configure a multi-layer neural network, we can do it in the following way:
@@ -224,7 +224,7 @@ It is also easy to make prediction and evaluate
 
 
 ```r
-preds = predict(model, test.x)
+preds <- predict(model, test.x)
 ```
 
 ```