cscherrer

Chads-MacBook-Pro:02-spark chad$ scp -i "~/aws-metis.pem" -v Spark_Supervised_Machine_Learning.ipynb ubuntu@ec2-54-186-96-112.us-west-2.compute.amazonaws.com:~/notebooks
Executing: program /usr/bin/ssh host ec2-54-186-96-112.us-west-2.compute.amazonaws.com, user ubuntu, command scp -v -t ~/notebooks
OpenSSH_7.6p1, LibreSSL 2.6.2
debug1: Reading configuration data /Users/chad/.ssh/config
debug1: Reading configuration data /etc/ssh/ssh_config
debug1: /etc/ssh/ssh_config line 20: Applying options for *
debug1: /etc/ssh/ssh_config line 51: Applying options for *
debug1: Connecting to ec2-54-186-96-112.us-west-2.compute.amazonaws.com port 22.
debug1: Connection established.
debug1: key_load_public: No such file or directory

cscherrer

export ProbVector
using ArgCheck: @argcheck
using Parameters

import TransformVariables: RealVector,@calltrans, LogJacFlag, 
    VectorTransform, dimension, inverse!, inverse_eltype
using TransformVariables
"""
    (y, r, ℓ) = SIGNATURES
Given ``x ∈ ℝ`` and ``0 ≤ r ≤ 1``, return `(y, r′)` such that

cscherrer

using Pkg
Pkg.add.(
      ["Distributions"
    , "MonteCarloMeasurements"
    , "StatsFuns"
    , "LaTeXStrings"
    , "Plots"])


using Distributions

cscherrer

# obs is a zero-one vector of truth
# prob is a vector resulting from "predict_proba"
def makecost(obs,prob,falsepos_cost,falseneg_cost):
    def cost(cutoff):
        pred = np.array(prob > cutoff)
        fpos = pred * (1 - obs) 
        fneg = (1 - pred) * obs
        return np.sum(fpos * falsepos_cost + fneg * falseneg_cost)
    return np.vectorize(cost)

cscherrer

using Transducers
T = Transducers

using Transducers: R_,next,inner,start,complete,wrap,unwrap,wrapping

struct MeanVar <: Transducer
end

cscherrer

using Transducers
T = Transducers

struct MeanVar <: Transducer
end


function T.start(rf::T.R_{MeanVar}, result)
    private_state = (0, 0.0, 0.0)

cscherrer

import PyCall,SymPy
using MLStyle

stats = PyCall.pyimport_conda("sympy.stats", "sympy");
SymPy.import_from(stats)

sym(x) = SymPy.symbols(x);

macro ℓ(expr)
    args = @match expr begin

cscherrer

JuliaActuary/MortalityTables.jl
JuliaActuary/DynamicPolynomials.jl
JuliaActuary/FixedPolynomials.jl
JuliaActuary/MultivariateBases.jl
JuliaActuary/MultivariateMoments.jl
JuliaActuary/MultivariatePolynomials.jl
JuliaActuary/SemialgebraicSets.jl
JuliaActuary/StaticPolynomials.jl
JuliaActuary/TypedPolynomials.jl

cscherrer

using Soss
using Distributions

mstep = @model pars,state begin
    # Parameters
    α = pars.α        # Daily transmission rate
    βγ = pars.βγ        # Daily recovery rate, case fatality rate
    
    # Starting counts
    s0 = state.s      # Susceptible

cscherrer

# This file is machine-generated - editing it directly is not advised

[[AbstractAlgebra]]
deps = ["InteractiveUtils", "LinearAlgebra", "Markdown", "Random", "RandomExtensions", "SparseArrays", "Test"]
git-tree-sha1 = "7df2949bfd757e426897a4b579fbd5dc776ff8c9"
uuid = "c3fe647b-3220-5bb0-a1ea-a7954cac585d"
version = "0.12.0"

[[AbstractLattices]]
git-tree-sha1 = "f35684b7349da49fcc8a9e520e30e45dbb077166"