added some stuff

This commit is contained in:
Daniel Knüttel 2019-01-09 14:19:33 +01:00
parent 591a474795
commit 90d638cfae
9 changed files with 224 additions and 0 deletions

19
ex_29.py Normal file
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from math import exp
from random import uniform
import matplotlib.pyplot as plt
def f(n, f0=100):
result = f0
for i in range(n):
result *= exp(0.2 + uniform(-0.04, 0.04))
return result
X = list(range(350))
y1 = [f(x) for x in X]
y2 = [100*exp(0.2*x) for x in X]
plt.plot(X, y1, X, y2)
plt.show()

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ex_30.py Normal file
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from random import uniform
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize
def f(n, f0=100):
result = f0
for i in range(n):
result *= np.exp(0.2 + uniform(-0.04, 0.04))
return result
X = np.arange(0, 100, 1)
Y = np.array([f(int(x)) for x in X])
fitf = lambda x, k, a: k*np.exp(x*a)
(k, a), pcov = scipy.optimize.curve_fit(fitf, X, Y, p0=[100, 0.2])
fitted = lambda x: k*np.exp(x*a)
plt.plot(X, Y, X, fitted(X))
plt.show()

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ex_31.py Normal file
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import scipy.optimize
import numpy as np
from defusedxml import ElementTree
from collections import deque
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
fit_f1 = lambda x, K, alpha: K * np.exp(alpha * x)
fit_f2 = lambda x, a, b: a*x + b
data = {"Germany": deque()
, "France": deque()
, "Italy": deque()
# , "United States": deque()
# , "Angola": deque()
# , "China": deque()
}
with open("data/API_NY.GDP.MKTP.KN_DS2_en_xml_v2_10230884.xml") as fin:
tree = ElementTree.parse(fin)
for record in tree.getroot().find("data").findall("record"):
this_data = {field.get("name"): field.text for field in record.findall("field")}
if(this_data["Country or Area"] in data):
if(this_data["Value"] != None):
data[this_data["Country or Area"]].append((this_data["Year"], this_data["Value"]))
class Data(object):
def __init__(self, raw_data):
self.x = np.array([int(k) for k, v in raw_data])
self.y = np.array([float(v) for k, v in raw_data])
plots = deque()
for country, values in data.items():
values = Data(values)
popt1, pcov = curve_fit(fit_f1, values.x, values.y
, p0=[values.y[0], 1])
popt2, pcov = curve_fit(fit_f2, values.x, values.y
, p0=[values.y[0], (values.y[-1] - values.y[0])/(values.x[-1] - values.x[0])])
f1 = lambda x: fit_f1(x, popt1[0], popt1[1])
f2 = lambda x: fit_f2(x, popt2[0], popt2[1])
p1, = plt.plot(values.x, values.y, label="{}: real".format(country))
p2, = plt.plot(values.x, f1(values.x), label="%s: exponential fit, K=%.3e, $\\alpha$=%.3e" % (country, *popt1))
p3, = plt.plot(values.x, f2(values.x), label="%s: linear fit, a=%.3e, b=%.3e" % (country, *popt2))
plots.extend([p1, p2, p3])
plt.legend(handles=list(plots))
plt.show()

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ex_32.py Normal file
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import time
def bad_timeit(f):
def timeitwrapper(*args, **kwargs):
start = time.time()
result = f(*args, **kwargs)
stop = time.time()
elapsed = stop - start
print("the function", f.__name__, "has been executed in", elapsed, "s.")
return result
return timeitwrapper
def _recursive_fibonacci(n):
if n in (0, 1):
return 1
return _recursive_fibonacci(n - 1) + _recursive_fibonacci(n - 2)
@bad_timeit
def recursive_fibonacci(n):
return _recursive_fibonacci(n)
if( __name__ == "__main__"):
recursive_fibonacci(43)

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ex_33.py Normal file
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import numpy as np
import matplotlib.pyplot as plt
class BrownIterator(object):
def __init__(self, N, m):
self._N = N
self._max_m = m
self._i = 0
self._xs = None
self._ys = None
def __iter__(self):
self._xs = np.zeros(self._N)
self._ys = np.zeros(self._N)
self._i = 0
return self
def __next__(self):
self._i += 1
if(self._i > self._max_m):
raise StopIteration()
if(self._i == 1):
return self._xs, self._ys
theta = np.random.uniform(0, np.pi * 2, self._N)
self._xs = self._xs + np.cos(theta)
self._ys = self._ys + np.sin(theta)
return self._xs, self._ys
if( __name__ == "__main__"):
data = np.array([i for i in BrownIterator(1000, 321)])
print(data)
p1, = plt.plot(data[20,0], data[20,1], "ro", label="t = 20")
p2, = plt.plot(data[80,0], data[80,1], "bo", label="t = 80")
p3, = plt.plot(data[320,0], data[320,1], "go", label="t = 320")
plt.legend(handles=[p1, p2, p3])
plt.show()

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ex_34.py Normal file
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import numpy as np
import random
class RankPageCalculator(object):
def __init__(self, connections):
self._connections = connections
self._dim = len(connections)
self._hits = np.arange(0, self._dim, 1)
self._current = random.randint(0, self._dim - 1)
def run(self, n, alpha):
for i in range(n):
if(np.random.uniform(0, 1) < alpha):
self._current = random.randint(0, self._dim - 1)
self._hits[self._current] += 1
continue
uniform = np.random.uniform(0, 1, self._dim)
uniform[1 != self._connections[self._current]] = 0
self._current = uniform.argmax()
self._hits[self._current] += 1
return self._hits
adj = np.array([[0, 1, 1, 0, 0, 0, 0, 0, 0]
, [0, 0, 1, 0, 1, 0, 0, 0, 0]
, [1, 1, 0, 1, 0, 0, 0, 0, 0]
, [0, 0, 1, 0, 1, 0, 0, 0, 0]
, [0, 1, 0, 1, 0, 0, 0, 0, 0]
, [0, 0, 0, 0, 1, 0, 1, 0, 1]
, [0, 0, 0, 0, 0, 1, 0, 0, 0]
, [0, 0, 0, 0, 0, 0, 1, 0, 0]
, [0, 0, 0, 0, 0, 1, 1, 1, 0]], dtype=np.int16)
print(RankPageCalculator(adj).run(100000, 0.05))
print(RankPageCalculator(adj).run(100000, 0.3))
adj[4, 5] = 1
adj[5, 4] = 1
print(RankPageCalculator(adj).run(100000, 0.05))
print(RankPageCalculator(adj).run(100000, 0.3))

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import time
class counter_of_calls(object):
def __init__(self, f):
self._f = f
self._cnt = 0
def __call__(self, *args, **kwargs):
self._cnt += 1
result = self._f(*args, **kwargs)
print(self._f.__name__, "has been called", self._cnt, "times; last time:", time.time())
return result
@counter_of_calls
def f(x):
return x + 6
if( __name__ == "__main__"):
print(f(3))
print(f(5))

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def check_range(f):
def wrapper(**kwargs):
for k,v in

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