parthi2929

[markdown] Think of this: You are in a node 1 of tree below. You need to find a way out traversing all nodes.

[/markdown][wp_graphviz] graph G { node [shape=circle width=0.4 style=filled]; label="Graph 1"; ranksep = 0.1; nodesep=0.8; 1 -- 2;

	import networkx as nx
	import matplotlib.pyplot as plt
	from plotly.graph_objs import Scatter,Line,Marker,Figure, Data, Layout, XAxis,YAxis
	from plotly.offline import iplot,init_notebook_mode
	from math import floor


	init_notebook_mode(connected=True)

	"""

	import numpy as np
	from math import sin, cos, pi
	from matplotlib import pyplot as plt

	"""
	Helper functions for "Reconsctrucing Trajectories" project
	Author: Parthiban R
	"""
	class Vehicle:
	def __init__(self):

	import matplotlib.pyplot as plt
	import numpy as np
	from math import sqrt, pi
	from coinflipviz import autoformat
	from pytexit import py2tex

	def plot_bi_nor(df, fontsize=10, mu=0, sigma=1, C='1/(sigmasqrt(2pi))', E='-(((X-mu)/sigma)**2)/2', xstepsize=10):
	"""
	Given the dataframe with x, n(x), p(x) this provides one plot:
	x vs p(x) along with normal approx curve

	# SDSP = Sample Distribution of Sample Proportions
	# This is helper file for programmatic illustrations of SDSP concepts.

	from random import shuffle
	import pandas as pd

	def create_bernoulli_population(N, p):
	"""
	Given the total size of population N, probability of a specific outcome,
	and associated bernoulli variable as list (of outcomes), this returns a shuffled

	[markdown]
	Think of this: You are in a node 1 of tree below. You need to find a way out traversing all nodes.

	[wp_graphviz]
	graph G {
	node [shape=circle width=0.4 style=filled];
	label="Graph 1";
	ranksep = 0.1;
	nodesep=0.8;
	1 -- 2;