NAKASONE Hiroshi hiroshinakasone

## least_squares_fitting.py
#!/bin/env python

import numpy as np
import matplotlib.pyplot as plt


def polyfit(x, y, deg):
    coef = np.polyfit(x, y, deg)
    y1 = coef[deg]
    for i in range(0, deg):

## docker-vz.yaml
# A template to use Docker instead of containerd & nerdctl
# $ limactl start ./docker.yaml
# $ limactl shell docker docker run -it -v $HOME:$HOME --rm alpine

# To run `docker` on the host (assumes docker-cli is installed):
# $ export DOCKER_HOST=$(limactl list docker --format 'unix://{{.Dir}}/sock/docker.sock')
# $ docker ...

# This template requires Lima v0.8.0 or later
images:

## keybase.md

      
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                hiroshinakasone
                / keybase.md
            
            
              Created
              June 1, 2021 05:42
            
          
    Keybase proof

I hereby claim:

I am hiroshinakasone on github.
I am nakasone (https://keybase.io/nakasone) on keybase.
I have a public key ASAVOmyyQnnp2h0Mnsq_z24X0J5ZfxL0rbLxG8WUeDZniwo

To claim this, I am signing this object:

  
## file1.txt
前処理
本処理
後処理

## cos_sim.py
#!/usr/bin/env python
"""A script for caluclating cosine similarity"""

import csv
import sys

import numpy as np
from scipy import sparse

# import pdb; pdb.set_trace()

## gen_sine.py
#!/usr/bin/env python3

import math

def sine(amp, freq, ph=0.0, dur=1.0, sr=44100):
    o = 2.0 * math.pi * freq / float(sr)
    for t in range(int(sr * dur)):
        yield amp * math.sin(ph + o * t)

def main():
	#!/bin/env python

	import numpy as np
	import matplotlib.pyplot as plt


	def polyfit(x, y, deg):
	coef = np.polyfit(x, y, deg)
	y1 = coef[deg]
	for i in range(0, deg):
	# A template to use Docker instead of containerd & nerdctl
	# $ limactl start ./docker.yaml
	# $ limactl shell docker docker run -it -v $HOME:$HOME --rm alpine

	# To run `docker` on the host (assumes docker-cli is installed):
	# $ export DOCKER_HOST=$(limactl list docker --format 'unix://{{.Dir}}/sock/docker.sock')
	# $ docker ...

	# This template requires Lima v0.8.0 or later
	images:
	#!/usr/bin/env python
	"""A script for caluclating cosine similarity"""

	import csv
	import sys

	import numpy as np
	from scipy import sparse

	# import pdb; pdb.set_trace()
	#!/usr/bin/env python3

	import math

	def sine(amp, freq, ph=0.0, dur=1.0, sr=44100):
	o = 2.0 * math.pi * freq / float(sr)
	for t in range(int(sr * dur)):
	yield amp * math.sin(ph + o * t)

	def main():