RyotaBannai

import numpy as np
from numpy.linalg import inv
import statsmodels.api as sm

# from scratch
x = sm.add_constant(x) # add constant in the 0 index
b = inv(x.T.dot(x)).dot(x.T).dot(y)
yest_ols = np.array([b[2]*v**2 + b[1]*v + b[0] for v in x.T[0]])

# with using numpy.linalg.lstsq

RyotaBannai

{ //json_build_object
    "model": "myapp.person",
    "pk": 1,
    "fields": { //json_build_object
      "first_name": "John",
      "last_name": "Lennon"
    }
}

RyotaBannai

db=# \t        
db=# \a        
db=# \o file.json      
db=# select row_to_json(t) from meryota_question as t;
//'meryota_question' is just my database name. 

RyotaBannai

[
  {
    "model": "myapp.person",
    "pk": 1,
    "fields": {
      "first_name": "John",
      "last_name": "Lennon"
    }
  },
  {

RyotaBannai

db=# \t                                   //1st command
db=# \a                                   //2nd command
db=# \o file.json                         //3rd command
db=# SELECT json_agg(json_build_object(   //4thcommand
     'pk', t.id,'model','meryota.question', 'fields', 
     json_build_object('question_text',t.question_text, 
     'pub_date',t.pub_date))) from meryota_question as t;

RyotaBannai

$pg_restore --verbose --clean --no-acl --no-owner -h localhost -U myuser -d mydb latest.dump

RyotaBannai

$heroku pg:backups:capture 
//Make the latest backup file on Poshgres on heroku
$heroku pg:backups:download
//Download the latest backup file to the localhost(the name is latest.dump) 
//Reference is below

RyotaBannai

N=5
fig, ax = plt.subplots(1, N, figsize=(5, 2.5),
                       subplot_kw={'xticks':[], 'yticks':[]},
                       gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i in range(1, N+1):
    ax[i-1].imshow(Vt[i].reshape(62, 47), cmap='binary_r')

RyotaBannai

fig, ax = plt.subplots(2, 5, figsize=(5, 2.5),
                       subplot_kw={'xticks':[], 'yticks':[]},
                       gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i in range(5, 10):
    ax[0, i-5].imshow(faces.data[i].reshape(62,47), cmap='binary_r')
    ax[1, i-5].imshow(X[i].reshape(62,47), cmap='binary_r')

RyotaBannai

X = US.dot(Vt[:dim,:])
X = X + average_face