Python import numpy as np import matplotlib.pyplot as plt Implement three different methods to si...
In 17]:import numpy as np import matplotlib.pyplot as plt Implement three different methods to simulate a Poisson process (Nog 100 with parameter λ = 0.1 on the time interval [0, 100]. For each method, plot a trajectory of your simulated process 1. Method 1: In 17]:import numpy as np import matplotlib.pyplot as plt Implement three different methods to simulate a Poisson process (Nog 100 with parameter λ = 0.1 on the time interval [0, 100]. For each method, plot a...
IN PYTHON 3 GIVING THIS CODE %matplotlib inline import numpy as np import matplotlib.pyplot as plt from sklearn import datasets N_samples = 2000 X = np.array(datasets.make_circles(n_samples=N_samples, noise=0.05, factor=0.3)[0]) plt.scatter(X[:,0], X[:,1], alpha=0.8, s=64, edgecolors='white'); Use Spectral Clustering to cluster the points and visualize your result
# python graph # i want to add a caption below this graph import matplotlib.pyplot as plt import numpy as np import math #pylab inline from mpl_toolkits.mplot3d import Axes3D a = np.linspace(50,200,50) b = np.linspace(500,2000,50) c = np.linspace(5,20,50) plt.axes(projection='3d') for i in c: plt.plot(a,b,i) plt.title("title) plt.yaxis("y axis ") txt = "this is a graph of a bunch of lines" #cant get this to work plt.text(0.5, 0.05,0.7,0.1, txt, ha='center') ##cant get this to work plt.set_size_inches(7, 8,6, forward=True) #cant get this to...
python 1 import matplotlib.pyplot as plt 2 import numpy as np 3 4 abscissa = np.arange(20) 5 plt.gca().set_prop_cycle( ’ color ’ , [ ’ red ’ , ’ green ’ , ’ blue ’ , ’ black ’ ]) 6 7 class MyLine: 8 9 def __init__(self, * args, ** options): 10 #TO DO: IMPLEMENT FUNCTION 11 pass 12 13 def draw(self): 14 plt.plot(abscissa,self.line(abscissa)) 15 16 def get_line(self): 17 return "y = {0:.2f}x + {1:.2f}".format(self.slope, self.intercept) 18 19 def __str__(self):...
PYTHON import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split Our goal is to create a linear regression model to estimate values of ln_price using ln_carat as the only feature. We will now prepare the feature and label arrays. "carat" "cut" "color" "clarity" "depth" "table" "price" "x" "y" "z" "1" 0.23 "Ideal" "E" "SI2" 61.5 55 326 3.95 3.98 2.43 "2" 0.21 "Premium" "E" "SI1"...
In Python import numpy as np Given the array a = np.array([[1, 2, 3], [10, 20, 30], [100, 200, 300]]), compute and print the sums over all rows (should give [6, 60, 600]) the sums over all columns (the sum of he first column is 111) the maximum of the array the maxima over all rows the mean of the sub-array formed by omitting the first row and column the products over the first two columns (hint: look for an...
This is a python matplotlib question. So it would be great if you could show me in python method. I have this loadtxt that asked to plot histogram of wind gusts(column 3) that lie in direction angle(column 2) from min angle to max angle inclusively. I don't know how to include min_angle and max_angle into my codes. Histogram of wind gust speeds As before the file akaroawindgusts.txt contains hourly maximum wind gusts speeds at the Akaroa Electronic weather station (EW)...
python / visual studio Problem 1: Random Walk A random walk is a stochastic process. A stochastic process is a series of values that are not determined functionally, but probabilistically. The random walk is supposed to describe an inebriated person who, starting from the bar, intends to walk home, but because of intoxication instead randomly takes single steps either forward or backward, left or right. The person has no memory of any steps taken, so theoretically, the person shouldn't move...
python / visual studio Problem 1: Random Walk A random walk is a stochastic process. A stochastic process is a series of values that are not determined functionally, but probabilistically. The random walk is supposed to describe an inebriated person who, starting from the bar, intends to walk home, but because of intoxication instead randomly takes single steps either forward or backward, left or right. The person has no memory of any steps taken, so theoretically, the person shouldn't move...
help me with this. Im done with task 1 and on the way to do task 2. but I don't know how to do it. I attach 2 file function of rksys and ode45 ( the first is rksys and second is ode 45) . thank for your help Consider the spring-mass damper that can be used to model many dynamic systems -- ----- ------- m Applying Newton's Second Law to a free-body diagram of the mass m yields the...