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# Every thing is an object. | |
# Every interaction with an object is a message. | |
# You don’t instantiate classes; you clone other objects called prototypes. | |
# Objects remember their prototypes. | |
# Objects have slots. Slots contain objects, including method objects. | |
# A message returns the value in a slot or invokes the method in a slot. | |
# If an object can’t respond to a message, it sends that message to its prototype. | |
Vehicle := Object clone | |
Vehicle description := "Something to take you places" |
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################################## | |
## DAY 1 | |
# Evaluate 1 + 1 and then 1 + "one". Is Io strongly typed or weakly typed? | |
1 + 1 | |
1+"one" | |
# > Exception: argument 0 to method '+' must be a Number, not a 'Sequence' | |
# Io is Strong: it does not perform implicit casts between types. |
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# https://help.open.ac.uk/documents/policies/working-out-your-class-of-honours/files/50/honours-class-working-out.pdf | |
# Only 2nd and 3rd year courses count. | |
# Course, credits, grade, year | |
courses = [ | |
["M343", 30, "Distinction", 3], | |
["M249", 30, "Distinction", 2], | |
["M248", 30, "Distinction", 2], | |
["MST210", 60, "Grade 2 Pass", 2] | |
] |
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import numpy as np | |
probability = 1/10000 | |
wealth = 120000 | |
houseValue = 100000 | |
bound = probability * np.log(wealth - houseValue) \ | |
+ (1 - probability) * np.log(wealth) | |
maxPremium = wealth - np.exp(bound) |
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from scipy.spatial.distance import cdist | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# First write a covariance function. e.g. rbf | |
def radial_basis_kernel(x1, x2, varSigma, lengthScale): | |
if x2 is None: | |
d = cdist(x1, x1) | |
else: | |
d = cdist(x1, x2) |
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import math | |
import scipy.stats as st | |
# assumes bivariate normal, dichotomised groups | |
def dichotomy_r_to_d(r) : | |
d = 2*r / (math.sqrt(1 - r**2)) | |
return d | |
# Equation 9 | |
# https://sci-hub.tw/10.1037/1082-989X.11.4.386 |
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import pandas as pd | |
# From https://www.notion.so/977d5e5be0434bf996704ec361ad621d?v=fe54f89ca9e04ac799af42b39e1efc4b | |
path = "COVID 19 Containment measures data.csv" | |
df = pd.read_csv(path) | |
withoutUS = df[~df["Country"].str.contains("US:")] | |
withoutUS = withoutUS[~withoutUS["Country"].str.contains("United States")] | |
numCountries = withoutUS.Country.unique().shape[0] |
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#%tensorflow_version 2.x | |
import pandas as pd | |
import numpy as np | |
import re | |
from nltk import word_tokenize | |
from nltk.stem import WordNetLemmatizer | |
from scipy.sparse import hstack | |
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer |
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def handle_utterance_str(text) : | |
if text[0] != "'" and text[0] != '"' : | |
text = f'"{text}"' | |
text = text.replace('"', '\"') | |
text = text.replace("'", '\"') | |
return "handle_utterance(1,{},Output)".format(text) | |
def escape_and_call_prolexa(text) : | |
libPrefix = "prolexa:" |
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#!/usr/bin/env python | |
# coding: utf-8 | |
# ## Excess COVID-19 mortality vs reported deaths over time | |
# | |
# split bar? | |
# | |
# y-axis: deaths | |
# x-axis: time | |
# |