RyotaBannai

from numpy import exp, array, random, dot, reshape
from autograd import grad 

class NeuronLayer():
    def __init__(self, neuron_n, inputs_n):
        self.weights = 2 * random.random((inputs_n, neuron_n)) - 1

class NeuralNetwork():
    def __init__(self, layer1, layer2):
        self.layer1 = layer1

RyotaBannai

RyotaBannai

RyotaBannai

RyotaBannai

c=['#415952', '#f35134', '#243AB5']
def dataset_ (n=200, idx_outlier=0, ydistance=5):
    rng = np.random.RandomState(4)
    data  = np.dot(rng.rand(2, 2), rng.randn(2, n)).T
    data[idx_outlier:idx_outlier+1,1] = ydistance
    return data

N=200
inx=10
i = dataset_(N, inx)

RyotaBannai

import numpy as np
x=np.array([1,4,7,2,5,7,7,8,4,6,8,30])
z=(x-x.mean(axis=0))/x.std()
#
for l in (z < z.mean() - 3*z.std(), 
          z > z.mean() + 3*z.std()):
     if np.any(z[l]):
            print(z[l])

RyotaBannai

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
plt.style.use('ggplot')

fig, (ax1, ax2) = plt.subplots(ncols=2,figsize=(16,6))
plt.xlim((0, 10))
plt.ylim((0, 7))
plt.tight_layout(w_pad=1.5)
#red line

RyotaBannai

N=60
mu=0
sd=2

np.random.seed(0)
ran = np.random.normal(size=N)
error1 = sd**2 * ran + mu
error2 = sd*.5 * ran + mu

lin = np.linspace(-15., 15., num=N)

RyotaBannai

import scipy.odr as odr

def odr_line(B, x):
    y = B[0]*x + B[1]*x**2
    return y

def perform_odr(x, y, xerr, yerr):
    quadr = odr.Model(odr_line)
    mydata = odr.Data(x, y, wd=1./xerr, we=1./yerr)
    #mydata = odr.Data(x, y)

RyotaBannai

import numpy.linalg as la
def tls(X,y):
    
    if X.ndim is 1: 
        n = 1 # the number of variable of X
        X = X.reshape(len(X),1)
    else:
        n = np.array(X).shape[1] 
    
    Z = np.vstack((X.T,y)).T