Changed comments for the normal distribtuion
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Jacob.Fritsche committed
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1 file changed
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-38
+27
-38
@@ -12,7 +12,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 25,
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"id": "a4ea5fe4-eaa9-4cc4-8677-385a68577332",
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"metadata": {},
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"outputs": [],
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@@ -27,44 +27,49 @@
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"id": "df08071e-cd3c-49dd-8623-baf38082eab0",
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"metadata": {},
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"source": [
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"Generate a normal distribution"
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"Generate a normal distribution with sample of an experiment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 35,
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"id": "280f78b3-4338-49ec-8929-732d429762db",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Generate 1000 random numbers from a normal distribution\n",
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"mu, sigma = 3, 6 # mean and standard deviation\n",
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"data = np.random.normal(mu, sigma, 1000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7f0ec0a0-ff5f-4fb1-93ae-509cf3d3b066",
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"metadata": {},
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"outputs": [],
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"source": [
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"data = np.random.normal(mu, sigma, 1000)\n",
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"mean_sample = data.mean().item()\n",
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"# Create a histogram of the data\n",
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"plt.hist(data, bins=30, density=True, alpha=0.6, color='g')\n",
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"title = \"Histogram with random numbers from: mu = %.2f, std = %.2f\" % (mu, sigma)\n",
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"plt.axvline(x=mean_sample , color='m', linestyle='--', label='mu sample = %.2f' % (mean_sample))\n",
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"plt.legend()\n",
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"plt.title(title)\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 29,
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"id": "0df9c2e5-ae6d-48c0-93a4-12a6e70240c2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
@@ -85,39 +90,23 @@
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "817835f6-baf8-4b03-8136-426c81b7b4fa",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Show the second normal distribution\n",
|
||||
"mu2, sigma2 = 1, 1\n",
|
||||
"p2 = stats.norm.pdf(x, mu2, sigma2)\n",
|
||||
"plt2.plot(x, p2, 'r', linewidth=2)\n",
|
||||
"title = \"Fit results: mu1 = %.2f, std1 = %.2f; mu2 = %.2f, std2 = %.2f\" % (mu, sigma, mu2, sigma2)\n",
|
||||
"plt2.title(title)\n",
|
||||
"plt2.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f3c0c504-e041-4d85-8da0-3ef766f8e87a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Generate a second normal distribution"
|
||||
"Generate for the distribution of mu_sample"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"execution_count": 36,
|
||||
"id": "f7118230-fc88-4a71-b813-2daa0c63c97c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
@@ -160,13 +149,13 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"execution_count": 37,
|
||||
"id": "8b4766b1-6fb5-487b-b552-790f43f83707",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
@@ -260,7 +249,7 @@
|
||||
"id": "63072dac-c7e6-4237-b5cc-b9832a2c7707",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now with narrowed mu"
|
||||
"Now with narrowed mu and alpha error"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in new issue
Block a user