vedraiyani

WillKoehrsen

import numpy as np
from hyperopt import hp, tpe, fmin

# Single line bayesian optimization of polynomial function
best = fmin(fn = lambda x: np.poly1d([1, -2, -28, 28, 12, -26, 100])(x),
            space = hp.normal('x', 4.9, 0.5), algo=tpe.suggest, 
            max_evals = 2000)

geffy

# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking. 

geffy

# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example of a simple method that performs bagging

"""

macromaniac

import numpy as np
from keras.layers import Dense, Input
from keras.models import Model

x = Input((1,))
y = Dense(1, activation ='linear')(x)

m = Model(x,y)
m.compile(loss = 'mse', optimizer='sgd')
_x = np.linspace(1,2, num = 1e3)

captainsafia

from datetime import date

sundays=0
for year in range(1901,2001):
	for month in range(1,13):
		if date(year,month,1).weekday()==6:
			sundays+=1
print sundays