Model of ncove severe disease in newborn

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mark mcgary

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Jun 11, 2020, 11:18:24 AM6/11/20
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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()

>>> tf.add(1, 2)

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:

Data = json.load (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:

Data = json.load (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:

Data = json.load (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:

Data = json.load (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()

>>> tf.add(1, 2)

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:

Data = json.load (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:

Data = json.load (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:

Data = json.load (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:

Data = json.load (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:





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