lorenzoriano

import numpy as np
from scipy import optimize
from matplotlib import pyplot as plt, cm, colors

def calc_R(x,y, xc, yc):
    """ calculate the distance of each 2D points from the center (xc, yc) """
    return np.sqrt((x-xc)**2 + (y-yc)**2)

def f(c, x, y):
    """ calculate the algebraic distance between the data points and the mean circle centered at c=(xc, yc) """

lorenzoriano

[global_config]
  focus = mouse
[keybindings]
  toggle_zoom = F12
  next_tab = <Shift>Right
  split_vert = <Ctrl><Shift>l
  split_horiz = <Ctrl><Shift>b
  prev_tab = <Shift>Left
[profiles]
  [[default]]

lorenzoriano

#!/bin/bash

#echo "This script prints the filenames of any dylibs in your /opt/ros/indigo/lib that depend on the System Python"
echo "This script prints the filenames of any dylibs in your /usr/local/ that depend on the System Python"
#for f in `find /usr/local/lib`; do
for f in `find /opt/ros/indigo/lib`; do
  otool -L "$f" 2> /dev/null| grep Python | grep System &> /dev/null
  status=$?
  if [ $status -eq 0 ]; then
    echo "$status: $f"

lorenzoriano

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np

intercept = -2.0
beta = 5.0
n_samples = 1000
regularization = 1e30
X = np.random.normal(size=(n_samples,1))

lorenzoriano

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np

NSAMPLE=5000
x_data = np.float32(np.random.uniform(-10.5, 10.5, (1, NSAMPLE))).T
r_data = np.float32(np.random.normal(size=(NSAMPLE,1)))
y_data = np.float32(np.sin(0.75*x_data)*7.0+x_data*0.5+r_data*1.0)
x_data, y_data = y_data, x_data #swap x and y