In [1]: import numpy as np
In [2]: import statsmodels.api as sm In [3]: import statsmodels.formula.api as smf # Load data In [4]: dat = sm.datasets.get_rdataset("Guerry", "HistData").data # Fit regression model (using the natural log of one of the regressors) In [5]: results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit() # Inspect the results In [6]: print(results.summary())
import matplotlib
matplotlib.rc('xtick', labelsize=20)
matplotlib.rc('ytick', labelsize=20)
matplotlib.rc('font',family='
import matplotlib.pyplot as plt
SMALL_SIZE = 8
MEDIUM_SIZE = 10
BIGGER_SIZE = 12
plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=SMALL_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=MEDIUM_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize
plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title
matplotlib.rc('xtick', labelsize=20)
matplotlib.rc('ytick', labelsize=20)
matplotlib.rc('font', size=20)
matplotlib.rc('axes', titlesize=20)
matplotlib.rc('axes', labelsize=20)
matplotlib.rc('legend', fontsize=20)
matplotlib.rc('figure', titlesize=20)
#size=25
size=15
params = {'legend.fontsize': 'large',
'figure.figsize': (20,8),
'axes.labelsize': size,
'axes.titlesize': size,
'xtick.labelsize': size*0.75,
'ytick.labelsize': size*0.75,
'axes.titlepad': 25}
plt.rcParams.update(params)
Here, we are running Beta-PERT Monte Carlo simulation.
from scipy import stats as stats
from scipy.stats import beta as beta
from scipy.stats import rv_continuous
import matplotlib.pylab as plt
class Beta_PERT(rv_continuous):
def _shape(self, minimum, mode, maximum, lamb):
alpha = 1+lamb*(mode-minimum)/(maximum-minimum)
beta = 1+lamb*(maximum-mode)/(maximum-minimum)
return [alpha,beta]
def _cdf(self,x, minimum, mode, maximum, lamb):
s_alpha, s_beta = self._shape(minimum, mode, maximum, lamb)
z = (x-minimum)/(maximum-minimum)
cdf = beta.cdf(z,s_alpha,s_beta)
return cdf
pert = Beta_PERT(name="pert")
rv_1 = pert(0.02,0.05,0.2,4)
rv_2 = pert(1,5,20,4)
N = 5000
freq = rv_1.rvs(N)
loss = rv_2.rvs(N)
ALE = freq*loss
http://www.chioka.in/differences-between-l1-and-l2-as-loss-function-and-regularization/
https://en.wikipedia.org/wiki/Deterministic_finite_automaton
Check a valid number or a valid decimal number DFA graph:
class Solution(object):
def isNumber(self, s):
# This is the DFA we have designed above
dfa = [
{"digit": 1, "sign": 2, "dot": 3},
{"digit": 1, "dot": 4, "exponent": 5},
{"digit": 1, "dot": 3},
{"digit": 4},
{"digit": 4, "exponent": 5},
{"sign": 6, "digit": 7},
{"digit": 7},
{"digit": 7}
]
current_state = 0
for c in s:
if c.isdigit():
group = "digit"
elif c in ["+", "-"]:
group = "sign"
elif c in ["e", "E"]:
group = "exponent"
elif c == ".":
group = "dot"
else:
return False
if group not in dfa[current_state]:
return False
current_state = dfa[current_state][group]
return current_state in [1, 4, 7]
I have been thinking of this problem for a while now. How human's memory is trigged? For example, you barely think of an event (your friend and you had a great conversation in a club on a rainy night) which happened 20 years ago. However, when you pick up a pen, which was given to you by your friend. Then you suddenly remember some content of the club conversation which took place 20 years ago.
How does a neural network model such brain activity? Here I am proposing a concept of super neuron. This super neuron is a loosely connected neuron in the NN with both a low probability and an associated event (or event summary) stored deeply inside the network.
The event associated with the super neuron has to be trigged by the event. Even more, the probability after the event registered with the system and then the memory flashing up is extremely low.
How to Supercharge Your Python Classes with Class Methods | by Siavash Yasini | May, 2024 | Towards Data Science As we just mentioned, a c...
