A "Best of the Best Practices" (BOBP) guide to developing in Python.
- "Build tools for others that you want to be built for you." - Kenneth Reitz
- "Simplicity is alway better than functionality." - Pieter Hintjens
// train split | |
let (x_train, x_test, y_train, y_test) = train_test_split(&xmatrix.unwrap(), &y, 0.3, true); | |
// model | |
let linear_regression = LinearRegression::fit(&x_train, &y_train, Default::default()).unwrap(); | |
// predictions | |
let preds = linear_regression.predict(&x_test).unwrap(); | |
// metrics | |
let mse = mean_squared_error(&y_test, &preds); | |
println!("MSE: {:?}", mse); |
let target_array = target.unwrap().to_ndarray::<Float64Type>().unwrap(); | |
// create a vec type and populate with y values | |
let mut y: Vec<f64> = Vec::new(); | |
for val in target_array.iter(){ | |
y.push(*val); | |
} |
let target_array = target.unwrap().to_ndarray::<Float64Type>().unwrap(); | |
// create a vec type and populate with y values | |
let mut y: Vec<f64> = Vec::new(); | |
for val in target_array.iter(){ | |
y.push(*val); | |
} |
pub fn convert_features_to_matrix(in_df: &DataFrame) -> Result<DenseMatrix<f64>>{ | |
/* function to convert feature dataframe to a DenseMatrix, readable by smartcore*/ | |
let nrows = in_df.height(); | |
let ncols = in_df.width(); | |
// convert to array | |
let features_res = in_df.to_ndarray::<Float64Type>().unwrap(); | |
// create a zero matrix and populate with features | |
let mut Xmatrix: DenseMatrix<f64> = BaseMatrix::zeros(nrows, ncols); | |
// populate the matrix |
{ "@context": { | |
"rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#", | |
"rdfs": "http://www.w3.org/2000/01/rdf-schema#", | |
"owl": "http://www.w3.org/2002/07/owl#", | |
"express": "http://example.com/express#", | |
"defines": { | |
"@reverse": "rdfs:isDefinedBy" | |
}, | |
"propertyOf": { | |
"@id": "rdfs:domain", |
#include <stdio.h> | |
#include <assert.h> | |
#include <msgpack.h> | |
typedef struct some_struct { | |
uint32_t a; | |
uint32_t b; | |
float c; | |
} some_struct; |