# Model of ncove severe disease in newborn

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### mark mcgary

Jun 11, 2020, 11:18:24 AM6/11/20
to packrat-discuss
Hello Packrat guru members.

I humbly introduce pathogen-primate intercept,

code as a newbie ..with a real purpose.

That of causing review at neonate, ped cohort baseline at severe disease. nCoV.

Why this may be of substance, shown via a photo of a smiling infant, report of mortality, 8 hours.

I refuse to stand idle.  And, request any interested code assistance.

McGary  *mmcg...@gmail.com   *amplifying data *research gate

preliminary ...attempt to execute sort as cleansed lawn, pathogen-host, *ncov 2019.

# Python program for

# Creation of Arrays

import numpy as np

# Creating a rank 1 Array

arr = np.array([1, 2, 3])

print("Array with Rank 1: \n",arr)

# Creating a rank 2 Array

arr = np.array([[1, 2, 3],

[4, 5, 6]])

print("Array with Rank 2: \n", arr)

# Creating an array from tuple

arr = np.array((1, 3, 2))

print("\nArray created using "

"passed tuple:\n", arr)

}

}

\$ python

>>> import tensorflow as tf

>>> tf.enable_eager_execution()

3

\$ Python: GUI

Import JSON

Import Numpy as np

Import tensor flow as tf

Model parameters

Class{var=x}yes=+1

X=tf variable {[tf float32]}

B=tf variable {[tf float32]}

Model input and output

X = tf placeholder [{(tf float 32)]}

B = tf variable [{.tf float 32)]}

Linear _model =a x X x b

Y=if placeholder (tf float 32)

A=loss

Loss – if reduce sum (tf float32)

Numpy protein sort at JSON:

`protein sort tf float= LLrrkk301 ldigpagplm gvvqygdnpa thfnlkthtn srdlktaiek itqrgglsnv grtisfvtkn`
`       ffskangnrs gapnvvvvmv dgwptdkvee asrlarvsgi niffitiega aenekqyvve`
`       pnfankavcr tngfyslhvq swfglhktlq plvkrvcdtd rlacsktcln sadigfvidg`
`       sssvgtgnfr tvlqfvtnlt kefeisdtdt rigavqytye qrlefgfdky sskpdilnai`
`       krvgywsggt stgaainfal eqlfkk`

{

"array": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"a": "g",

"c": "d",

"e": "f"

"h":"i":"k":"l":"m":"n":"p":"q";"r";"S":"t":"v":"w":"y":},

"string": ""

}

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array= 4": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"aa": "gg",

"cc": "dd",

"ee": "ff"

"hh":"ii":"kk":"ll":"mm":"nn":"pp":"qq";

"rr";"ss":"tt":"vv":"ww":"yy":},

"string": ""

}’’’

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array = 5": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"aaa": "ggg",

"ccc": "ddd",

"eee": "fff"

"hhh":"iii":"kkk":"lll":"mmm":"nnn":

"ppp":"qqq";"rrr";"sss":"ttt":"vvv":

"www":"yyy":"x";"xx"},

"string": ""

}

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:’’’

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"xax": "xgx",

"xcx": "xdx",

"xex": "xfx"

"xhx":"xix":"xkx":"xlx":"xmx":"xnx":

"xpx":"xqx";"xrx";"xsx":"xtx":"xvx":

"xwx":"xyx":},

"string": ""

}’’’

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:

NEXT Model

Cleansed lawn via observed constraint:

Exact to:

TGVHNGGVTSALTTVASAGLLSQLANGVIALNL

x <- rnorm(1000)

hx <- hist(x, breaks=100, plot=FALSE)

plot(hx, col=ifelse(abs(hx\$breaks) < 1.669, 4, 2))

}

}

\$ python

>>> import tensorflow as tf

>>> tf.enable_eager_execution()

3

\$ Python: GUI

Import JSON

Import Numpy as np

Import tensor flow as tf

Model parameters

Class{var=x}yes=+1

X=tf variable {[tf float32]}

B=tf variable {[tf float32]}

Model input and output

X = tf placeholder [{(tf float 32)]}

B = tf variable [{.tf float 32)]}

Linear _model =a x X x b

Y=if placeholder (tf float 32)

A=loss

Loss – if reduce sum (tf float32)

Numpy protein sort at JSON:

protein sort tf float= LLrrkktgvhnggvtsalttvasagllsqlangvialnl

{

"array": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"a": "g",

"c": "d",

"e": "f"

"h":"i":"k":"l":"m":"n":"p":"q";"r";"S":"t":"v":"w":"y":},

"string": ""

}

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array= 4": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"aa": "gg",

"cc": "dd",

"ee": "ff"

"hh":"ii":"kk":"ll":"mm":"nn":"pp":"qq";

"rr";"ss":"tt":"vv":"ww":"yy":},

"string": ""

}’’’

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array = 5": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"aaa": "ggg",

"ccc": "ddd",

"eee": "fff"

"hhh":"iii":"kkk":"lll":"mmm":"nnn":

"ppp":"qqq";"rrr";"sss":"ttt":"vvv":

"www":"yyy":"x";"xx"},

"string": ""

}

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g:’’’

Python: GUI

Import JSON

Import Numpy

Numpy protein sort at JSON:

’’’{

"array": [

1,

2,

3

],

"boolean": true,

"null": null,

"number": 123,

"object": {

"xax": "xgx",

"xcx": "xdx",

"xex": "xfx"

"xhx":"xix":"xkx":"xlx":"xmx":"xnx":

"xpx":"xqx";"xrx";"xsx":"xtx":"xvx":

"xwx":"xyx":},

"string": ""

}’’’

Import json

Import numpy as np

With open (‘training-data-10k.json’) as f:

Xs = np.array (data [‘ys’])

YS = np.array (data[‘ys’])

X – train = xs[-10]

Y-train= ys [: -10]

x-train = xs[: -10]

x-test= xs [:-10]

y-test = ys[:-10]

open training data as g:

open(‘training_data -10k.jason’) as g: