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import boto3 | |
import pandas as pd | |
def read_s3(bucket, prefix): | |
s3 = boto3.resource("s3") | |
bucket = s3.Bucket(bucket) | |
prefix_objs = bucket.objects.filter(Prefix=prefix) | |
df = pd.concat([pd.read_excel(obj.get()["Body"].read()) for obj in prefix_objs]) |
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class BinaryTree: | |
def __init__(self, root_val): | |
self.root = root_val | |
self.left = None | |
self.right = None | |
def insert_left(self, node): | |
if self.left is None: | |
self.left = BinaryTree(node) | |
else: |
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import numpy as np | |
import pandas as pd | |
from datetime import datetime | |
# make an example | |
np.random.seed(0) | |
item = np.random.choice(['A', 'B'], 10) | |
year = np.random.choice([2016, 2017], 10) |
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iris2 <- as.matrix(iris[, 1:4]) | |
# cetralize data | |
centered_iris2 <- scale(iris2, center = TRUE, scale = FALSE) | |
# covariance matrix | |
cov_iris2 <- (t(centered_iris2) %*% centered_iris2) / (dim(centered_iris2)[1] - 1) | |
# perform eigendecomposition on covariance matrix | |
eig_cov <- eigen(cov_iris2) |
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from ad import adnumber | |
from ad import admath | |
import numpy as np | |
# ref: http://cs231n.github.io/optimization-2/ | |
w = adnumber(np.array([2, -3, -3])) | |
x = adnumber(np.array([-1, -2, 1])) | |
f = 1 / (1 + admath.exp(-sum(w * x))) |
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#!usr/bin/env python3 | |
# -*- coding: utf-8 -*- | |
import numpy as np | |
def get_alpha(error): | |
return 0.5 * np.log((1 - error) / error) | |
def update_weights(y, weights, g, alpha): | |
return weights * np.exp(-alpha * g * y) / sum(weights * np.exp(-alpha * g * y)) |
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#!usr/local/env python3 | |
# -*- coding: utf-8 -*- | |
import itertools | |
def consonant(c): | |
return c.lower() not in "aeiou" | |
# (built-in) filter() | |
list(filter(consonant, 'Aartttik')) | |
list(itertools.filterfalse(consonant, 'Aartttik')) |