Welcome to the company ! we have many years of professional experience !
systeelplate@outlook.com +86 13526880645

Stainless Steel 316 Pipe Fittings

Henan Shang Yi Steel Trade Co., Ltd. is an enterprise specializing in steel sales and processing, cargo transportation and other services. It is committed to the production of wear-resistant steel plates, low-alloy high-strength plates, boiler vessel steel plates, composite steel plates, and extra-wide and extra-thick steel plates. Professional services such as bulk sales, warehousing, cutting and distribution. Products cover mining equipment, cement machinery, metallurgical machinery, construction equipment, ship equipment, power equipment, port equipment, transportation and general machinery manufacturing and other industries. The company's steel plate processing plant can cut semi-finished products and special-shaped parts according to user requirements, and can transport on behalf of customers. It is sold all over the country and exported overseas, and has won praise and trust from customers and markets.

Certificate of Honor

CONTACT US

Customer satisfaction is our first goal!

Phone

+86 13526880645

E-Mail

systeelplate@outlook.com

Address

No.186, Zi Dong Road, Guan Cheng District, Zheng Zhou, He Nan Province.

Stainless Steel 316 Pipe Fittings
PCA Mini Project - Rhys Shea
PCA Mini Project - Rhys Shea

# Download the data, if not already on disk and load it as numpy arrays ,lfw,_,people, = ,fetch_lfw_people, ('data', min_faces_per_person = 70, resize = 0.4) # introspect the images arrays to find the shapes (for plotting) n_samples, h, w = ,lfw,_,people,. images. shape np. random. seed (42) # for machine learning we use the data directly (as relative pixel # position info is ignored by this model) X ...

jenayl’s gists · GitHub
jenayl’s gists · GitHub

from sklearn. datasets import ,fetch_lfw_people,: ,people, = ,fetch_lfw_people, (min_faces_per_person = 20, resize = 0.7) 1 file 0 forks 0 comments 0 stars jenayl / mnist_tsne.py. Created Aug 2, 2017. View mnist_tsne.py. from time import time: import numpy as np: import matplotlib. ...

#Importing the datafrom sklearn.datasets import fetch_lfw ...
#Importing the datafrom sklearn.datasets import fetch_lfw ...

from sklearn. datasets import ,fetch_lfw_people people, = ,fetch_lfw_people, ( min_faces_per_person = 20 , resize = 0.7 ) image_shape = ,people,. images [ 0 ] . shape

Dimensionality Reduction and Feature Transformation ...
Dimensionality Reduction and Feature Transformation ...

# Data of famous ,people,'s faces faces = ,fetch_lfw_people, (min_faces_per_person = 70, resize = 0.4) X = faces. data y = faces. target target_names = faces. target_names n_classes = target_names. shape [0] # Split data X_train, X_test, y_train, y_test = train_test_split (X, y, test_size = 0.25, random_state = 42)

3.6. scikit-learn: machine learning in Python — Scipy ...
3.6. scikit-learn: machine learning in Python — Scipy ...

Ideally, we would use a dataset consisting of a subset of the Labeled Faces in the Wild data that is available with sklearn.datasets.,fetch_lfw_people,(). However, this is a relatively large download (~200MB) so we will do the tutorial on a simpler, less rich dataset. Feel free to explore the ,LFW, dataset.

import theanoimport osimport numpy as np from PIL import ...
import theanoimport osimport numpy as np from PIL import ...

lfw,_,people, = ,fetch_lfw_people, (funneled = True, resize = 0.4) # introspect the images arrays to find the shapes (for plotting) n_samples, h, w = ,lfw,_,people,. images. shape # for machine learning we use the 2 data directly (as relative pixel # positions info is ignored by this model)

Inquiry Email

Business cooperation

+86 13526880645

Company address

No.186, Zi Dong Road, Guan Cheng District, Zheng Zhou, He Nan Province.