Examples

Here are some side-by-side examples using the official Python scikit-learn package on the left and the TS sklearn package on the right.

StandardScaler

Python

import numpy as np
from sklearn.preprocessing import StandardScaler
 
data = np.array([
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
])
 
s = StandardScaler()
 
x = s.fit_transform(data)

TypeScript

import * as sklearn from 'sklearn'
 
const data = [
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
]
 
const py = await sklearn.createPythonBridge()
 
const s = new sklearn.StandardScaler()
await s.init(py)
 
const x = await s.fit_transform({ X: data })

KMeans

Python

import numpy as np
from sklearn.cluster import KMeans
 
data = np.array([
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
])
 
model = KMeans(
  n_clusters=2,
  random_state=42,
  n_init='auto'
)
 
x = model.fit_predict(data)

TypeScript

import * as sklearn from 'sklearn'
 
const data = [
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
]
 
const py = await sklearn.createPythonBridge()
 
const model = new sklearn.KMeans({
  n_clusters: 2,
  random_state: 42,
  n_init: 'auto'
})
await model.init(py)
 
const x = await model.fit_predict({ X: data })

TSNE

Python

import numpy as np
from sklearn.manifold import TSNE
 
data = np.array([
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
])
 
model = TSNE(
  n_components=2,
  perplexity=2,
  learning_rate='auto',
  init='random'
)
 
x = model.fit_transform(data)

TypeScript

import * as sklearn from 'sklearn'
 
const data = [
  [0, 0, 0],
  [0, 1, 1],
  [1, 0, 1],
  [1, 1, 1]
]
 
const py = await sklearn.createPythonBridge()
 
const model = new sklearn.TSNE({
  n_components: 2,
  perplexity: 2,
  learning_rate: 'auto',
  init: 'random'
})
await model.init(py)
 
const x = await model.fit_transform({ X: data })

Restrictions Motivation